Turbulence-resistant free space optical communication via chaotic block-matching and 3D filtering.

In this paper, we propose a chaotic block-matching and three-dimensional (C-BM3D) filtering algorithm to remove the noise and enhance the security in the turbulent channel of free space optical (FSO) communication. We experimentally demonstrate the performance of C-BM3D by comparing it with chaotic non-local means filtering (C-NLM), chaotic Gaussian filtering and chaotic Median filtering based on Log-normal and Gamma-Gamma turbulence models. The results show that the peak signal-to-noise ratios (PSNRs) of C-BM3D in the weak turbulence under Log-normal and Gamma-Gamma models are up to 96.2956 and 93.2853, respectively. The C-BM3D also achieves superior image similarity in Log-normal turbulent channel, with its structural similarity index measures (SSIMs) nearly equal to 1. Additionally, the signal-to-noise ratio (SNR) of C-BM3D ranks the highest, and its bit error rate (BER) improves by at least 15 dB compared to that of the other three algorithms. The experimental results indicate that the C-BM3D can be a good candidate for the next generation of FSO communication in security and turbulence resistance.

Two-stage look-up-table-based 16QAM vector millimeter-wave signal generation and reception.

We propose a two-stage look-up table (LUT) scheme for a photonic 16 quadrature-amplitude-modulation (QAM) millimeter-wave (MMW) communication system. The first-stage LUT is used at the transmitter, which can eliminate complex computational processes and adaptively adjust the precoded amplitude values to achieve optimal performance without being affected by half-wave voltage variations. We have completed a signal transmission below the hard-decision forward error correction (HD-FEC) threshold of 3.8 × 10-3 at the baud rate of 2/4 GBaud for weak turbulence and 2 GBaud for medium turbulence free-space optics (FSO) channel transmission. The second-stage LUT is used for post-compensation at the receiver as a nonlinear scheme that records the average pattern-related distortion of the channel and mitigates transmission impairment through nonlinear post-compensation. With the help of the second-stage LUT, the sensitivity of the optical receiver is improved by 0.15 dB at a baud rate of 2 GBaud for medium turbulence FSO channel transmission. As the baud rate increases to 4 GBaud, the system's bit error ratio (BER) can reach the soft-decision forward error correction (SD-FEC) threshold of 4.2 × 10-2 only after applying the second-stage LUT.

Investigating a Machine Learning Approach to Predicting White Pixel Defects in Wafers-A Case Study of Wafer Fabrication Plant F.

CMOS image sensor (CIS) semiconductor products are integral to mobile phones and photographic devices, necessitating ongoing enhancements in efficiency and quality for superior photographic outcomes. The presence of white pixels serves as a crucial metric for assessing CIS product performance, primarily arising from metal impurity contamination during the wafer production process or from defects introduced by the grinding blade process. While immediately addressing metal impurity contamination during production presents challenges, refining the handling of defects attributed to grinding blade processing can notably mitigate white pixel issues in CIS products. This study zeroes in on silicon wafer manufacturers in Taiwan, analyzing white pixel defects reported by customers and leveraging machine learning to pinpoint and predict key factors leading to white pixel defects from grinding blade operations. Such pioneering practical studies are rare. The findings reveal that the classification and regression tree (CART) and random forest (RF) models deliver the most accurate predictions (95.18%) of white pixel defects caused by grinding blade operations in a default parameter setting. The analysis further elucidates critical factors like grinding load and torque, vital for the genesis of white pixel defects. The insights garnered from this study aim to arm operators with proactive measures to diminish the potential for customer complaints.

Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.

Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.

Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients.

Background: The increase in the global population of hemodialysis patients is linked to aging demographics and the prevalence of conditions such as arterial hypertension and diabetes mellitus. While previous research in hemodialysis has mainly focused on mortality predictions, there is a gap in studies targeting short-term hospitalization predictions using detailed, monthly blood test data. Methods: This study employs advanced data preprocessing and machine learning techniques to predict hospitalizations within a 30-day period among hemodialysis patients. Initial steps include employing K-Nearest Neighbor (KNN) imputation to address missing data and using the Synthesized Minority Oversampling Technique (SMOTE) to ensure data balance. The study then applies a Support Vector Machine (SVM) algorithm for the predictive analysis, with an additional enhancement through ensemble learning techniques, in order to improve prediction accuracy. Results: The application of SVM in predicting hospitalizations within a 30-day period among hemodialysis patients resulted in an impressive accuracy rate of 93%. This accuracy rate further improved to 96% upon incorporating ensemble learning methods, demonstrating the efficacy of the chosen machine learning approach in this context. Conclusions: This study highlights the potential of utilizing machine learning to predict hospital readmissions within a 30-day period among hemodialysis patients based on monthly blood test data. It represents a significant leap towards precision medicine and personalized healthcare for this patient group, suggesting a paradigm shift in patient care through the proactive identification of hospitalization risks.