Bipolar segmented photonic digital-to-analog converter with wavelength multiplexing and balanced detection.

Photonic digital-to-analog converters (PDACs) with segmented design can achieve better performance than conventional binary PDACs in terms of effective number of bits (ENOB) and spurious-free dynamic range (SFDR). However, segmented PDACs generally require an increased amount of laser sources. Here, a structure of bipolar segmented PDAC based on laser wavelength multiplexing and balanced detection is proposed. The number of lasers is reduced by a half compared to a conventional segmented design with the same nominal resolution. Moreover, ideal bipolar output with no direct-current bias can be achieved with balanced detection. A proof-of-concept setup with a sampling rate of 10 GSa/s is constructed by employing only four lasers. The PDAC consists of four unary weighted channels and four ternary weighted channels. The measured ENOB and SFDR are 4.6 bits and 37.0 dBc, respectively. Generation of high-quality linear frequency-modulated radar waveforms with an instantaneous bandwidth of 4 GHz is also demonstrated.

Broadband linear frequency-modulated waveform generation based on optical frequency comb assisted spectrum stitching.

In this paper, we propose and demonstrate a novel spectrum stitching method for broadband linear frequency-modulated waveform (LFMW) generation. An optical frequency comb (OFC) is modulated by a narrowband LFMW whose bandwidth matches the free spectral range of the OFC. Optical injection locking is employed in extracting one broadband frequency sweeping component from the modulated OFC. In this way, seamless spectrum stitching is realized and a broadband LFMW with a multi-fold time-bandwidth product (TBWP) is obtained. Our scheme has a simple structure, which requires only a single OFC, a modulation module and a baseband waveform generator. An LFMW as broad as 20 GHz is generated from a baseband LFMW with 2GHz bandwidth experimentally. The TBWP is 100 times as large as that of the baseband LFMW. Moreover, the power fluctuation and the phase jumps are both eliminated, ensuring an excellent pulse compression performance. Benefiting from the injection locking technique, the linearity reaches 2.0 × 10-6. The central frequency tuning ability of our scheme is also demonstrated.

Noise analysis of photonic digital-to-analog converters.

Photonic digital-to-analog converters (PDACs) have a broad application prospect due to the ability to overcome the non-idealities in electronic circuits. PDACs are usually implemented by quantizing and summing the optical intensities of multiple lasers. The relative intensity noise of laser sources plays a critical role in determining the signal-to-noise ratio (SNR) and effective number of bits (ENOB). We present a detailed noise analysis for PDACs. Both the traditional binary-weighted structure and the recently proposed segmented-weighted structure are investigated. The results show that laser noise imposes a fundamental limit to the maximum SNR and ENOB that can be achieved in binary-weighted PDACs, while segmented PDACs can break this limitation and have a continuously increasing SNR with the quantization bit number (QBN). A novel configuration based on laser multiplexing and balanced detection, to the best of our knowledge, is also proposed and analyzed to increase the number of bits when the number of lasers is limited. Numerical simulations are performed to evaluate the SNR evolution with the QBN in different types of PDACs. The results are in good agreement with the theoretical analysis. Our analysis provides useful insights and can be important guidance for implementing high-performance PDACs.

Reconfigurable radar signal generator based on phase-quantized photonic digital-to-analog conversion.

A novel, to the best of our knowledge, scheme for reconfigurable radar signal generation is proposed based on the principle of photonic phase-quantized digital-to-analog conversion. Multi-level digital phase modulation with different modulation depths is combined to convert multi-channel digital data to the phase of an optical carrier. Frequency-modulated or phase-modulated radar signals are generated by beating the phase-synthesized optical carrier with a coherent reference light. The proposed radar signal generator features a simple structure, highly reconfigurable modulation format, and flexibly tunable frequency. A 3-bit photonic phase-quantized digital-to-analog converter with a 10-GSa/s sampling rate is constructed experimentally. The generation of linear frequency-modulated, nonlinear frequency-modulated, frequency-stepped, frequency-hopping, binary phase-coded, and polyphase-coded waveforms is demonstrated.

Association between ambient temperature and daily emergency hospitalizations for acute coronary syndrome in Yancheng, China.

Acute coronary syndrome (ACS) is a major public health concern worldwide. Few studies have directly evaluated the associations between ambient temperature and ACS incidence. To explore the association between ambient temperature and ACS emergency hospitalizations in the area of subtropical monsoon climate, data on ACS emergency hospitalizations were collected from two highest-ranking hospitals in the central urban area of Yancheng, China, from January 1, 2013, to December 31, 2018. We applied the time-series method to investigate the potentially lagged and non-linear effects of ambient temperature on ACS using the generalized linear model combined with the distributed lag non-linear model after adjusting for time trend, day of the week, holiday, and relative humidity. We identified a total of 5303 cases of ACS emergency hospitalizations during the study period. The exposure-response curves between ambient temperature and ACS hospitalizations were inverse "J-shaped." The effects of extreme low temperature on ACS hospitalizations occurred on the present day and lasted for 3 days, followed by the harvesting effect. The effects of extreme high temperature occurred on the present day and lasted for 5 days. The cumulative relative risks of ACS were 2.14 [95% confident interval (CI): 1.32 to 3.47] for extremely low temperature and 1.66 (95% CI: 1.33 to 2.06) for extremely high temperature over the lag of 0-5 days, compared with the reference temperature (25.0 °C). Both low and high temperatures were significantly associated with higher risks of emergency hospital admissions for ACS in Yancheng, China.

Optical spectrum feature analysis and recognition for optical network security with machine learning.

Physical layer attacks threaten services transmitted through optical networks. To detect attacks, we present an investigation of optical spectrum feature analysis (OSFA) and recognition. By analyzing the spectral features of optical signals, recognition and detection of unauthorized signals can be realized. In this paper, (1) we theoretically analyzed factors influencing optical spectrum (OS) features and simulated these factors. OSs collected from the simulation are quantitatively analyzed, spectral features are extracted by principal component analysis, and the theoretical derivation is validated. (2) We proposed support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN) machine-learning OSFA methods. (3) Experimentally collected OSs from commercial small form-factor pluggable modules are used to verify the performance of the SVM and 1D-CNN methods, which achieved 98.54% and 100% recognition accuracies, respectively, demonstrating that the methods are promising solutions for optical network security.

Association of fine particulate matter on acute exacerbation of chronic obstructive pulmonary disease in Yancheng, China.

BACKGROUND: Epidemiological evidence on the association between short-term exposure to fine particulate matter (PM2.5) air pollution and acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is limited in China. OBJECTIVES: To explore the associations between PM2.5 and AECOPD in Yancheng, China from 2015 to 2017. METHODS: In this time-series study, we used a generalized linear model with quasi-Poisson regression to investigate the association between PM2.5 and AECOPD admitted in two major hospitals in Yancheng. We tested the robustness of the associations using two-pollutant models and examined the potential effect modification by age, gender and season via stratification analyses. Lastly, we fitted the concentration-response curves. RESULTS: We identified a total of 4761 AECOPD inpatients during the study period. The average daily-mean PM2.5 concentration was 45.2 µg/m3. Each 10 µg/m3 increase in PM2.5 concentration on the concurrent day of the onset of AECOPD was associated with a 1.05% (95% confidence interval: 0.14%, 1.96%) increase in AECOPD. The association was robust to the adjustment of ozone, but not to sulfur dioxide, nitrogen dioxide, and carbon monoxide. The association was larger in females, elderly patients, and was restricted within the cold season, but all between-group differences were insignificant. The concentration-response relationship curves were generally linear but flatted at concentrations over 40 µg/m3. CONCLUSIONS: This study demonstrated a higher risk of AECOPD associated with present-day PM2.5 exposure in a Chinese city. We further provided important information on lag patterns, susceptible subgroups, sensitive seasons, as well as the characteristics of the concentration-response relationship curves.

Association between ambient particulate matter air pollution and ST-elevation myocardial infarction: A case-crossover study in a Chinese city.

BACKGROUND: Abundant epidemiological studies have revealed that short-term exposure to ambient air pollution increased the incidence of ischemic heart diseases. However, few investigations have explored the association between air pollution and ST-elevation myocardial infarction (STEMI), one major subtype of such events. METHODS: We conducted a time-stratified case-crossover study in two major hospitals of Yancheng, a city in East China, from January 2015 to February 2018. We used conditional logistic regression models to explore the association between hourly concentrations of air pollutants and STEMI hospitalizations. We explored potential effect modification in susceptible subgroups by age, gender, smoking status, and comorbidities. Two-pollutant models were fitted to test the robustness of the association. RESULTS: We identified a total of 347 STEMI patients. In single-pollutant models, each 10 µg/m3 increase in concentrations of fine and inhalable particulate matter (PM) (lag 13-24 h) was associated with increments of 5.27% [95% confidence interval (CI): 1.09%, 9.46%] and 3.86% (95%CI: 0.83%, 6.88%) in STEMI hospitalizations, respectively. We observed slightly larger associations of STEMI hospitalization with PM in patients who were older than 65, female, non-smoker, and with comorbidities (hypertension, diabetes or hyperlipidemia). The associations were generally robust to adjustment of criteria gaseous pollutants except for carbon monoxide. CONCLUSION: This is the first study in China that suggested acute exposure to elevated PM concentrations may trigger STEMI. Patients with cardiometabolic comorbidities were slightly more susceptible to air pollution.