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.

Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste.

Insights into carbon sources (biogenic and fossil carbon) and contents in solid waste are vital for estimating the carbon emissions from incineration plants. However, the traditional methods are time-, labor-, and cost-intensive. Herein, high-quality data sets were established after analyzing the carbon contents and infrared spectra of substantial samples using elemental analysis and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), respectively. Then, five classification and eight regression machine learning (ML) models were evaluated to recognize the proportion of biogenic and fossil carbon in solid waste. Using the optimized data preprocessing approach, the random forest (RF) classifier with hyperparameter tuning ranked first in classifying the carbon group with a test accuracy of 0.969, and the carbon contents were successfully predicted by the RF regressor with R2 = 0.926 considering performance-interpretability-computation time competition. The above proposed algorithms were further validated with real environmental samples, which exhibited robust performance with an accuracy of 0.898 for carbon group classification and an R2 value of 0.851 for carbon content prediction. The reliable results indicate that ATR-FTIR coupled with ML algorithms is feasible for rapidly identifying both carbon groups and content, facilitating the calculation and assessment of carbon emissions from solid waste incineration.

Hybrid digital-analog FSO fronthaul system with channel-adaptive insertion of analog bandwidth.

With the arrival of the 5th generation mobile network, the number of user devices is increasing exponentially, and thus it is necessary to expand the capacity of transmission systems. In order to further improve the system spectral efficiency on the basis of existing mobile fronthaul devices, we propose a hybrid digital-analog fronthaul transmission system with adaptive insertion of analog bandwidth, which can dynamically change the position of inserted analog bandwidth based on the state information of free space optical (FSO) channel. We consider the effects of atmospheric attenuation and turbulence on the FSO channel and derive an analytical expression for the maximum analog signal bandwidth that can be inserted into the first null of the digital signal spectrum to meet BER requirement of 3.8 × 10-3. Through a comprehensive simulation, it is verified that the analog bandwidth is obtained by this expression can exactly represent the lower bound of the simulation results under weak turbulence condition. The obtained results show that the maximum insertable analog bandwidth beyond the spectral null of the digital signal can reach 10% of the digital signal bandwidth, even in the FSO link with a transmission distance of 0.5 km and attenuation factor of 8 dB/km.

Secure turbulence-resistant coherent free-space optical communications via chaotic region-optimized probabilistic constellation shaping.

We propose a chaotic region-optimized probabilistic constellation shaping (CRPCS) scheme to enhance the security and the resistance to turbulence for free-space optical (FSO) communications. For this approach, a four-dimensional hyperchaotic system generates a pseudorandom sequence to rotate and encrypt the constellation. Constellation distribution of short pseudorandom sequences behaves as the law of a non-uniform character. Grouping long pseudorandom sequences and counting the characteristics of constellation distribution can realize probabilistic constellation shaping with low and fixed redundant information. We demonstrate a 56 Gbyte/s coherent FSO communication system based on log-normal and Gamma-Gamma turbulence models with a key space of 1075. The results show that the optical receiver sensitivity is improved by 0.3-1.1 dB, and the transmission distance is also improved by 3.2%-7.0% in different shaping cases.

Independent dual-single-sideband QPSK signal detection based on a single photodetector.

The two sidebands of the independent dual-single-sideband (dual-SSB) signal can carry different information to achieve higher spectral efficiency. However, the two sidebands of the independent dual-SSB vector signal are received independently. Generally, the receiver divides the signal into two channels. For each channel, we use an optical bandpass filter (OBPF) to select the left sideband (LSB) or right sideband (RSB), respectively. Then a photodetector (PD) is used for photoelectric conversion, followed by subsequent digital signal processing (DSP). To reduce the complexity and cost of the receiver, we propose a new independent dual-SSB vector signal detection scheme based on a single PD combined with conventional DSP. An electric bandpass filter (EBPF) filters out high-frequency components after photoelectric conversion, and then the signal is quadrature demodulated and processed by the DSP algorithm. The LSB and RSB are quadrature phase-shift keying (QPSK) modulated with an initial phase difference of π/4. Simulation results show that the proposed scheme performs better bit error rate (BER). For back-to-back (BTB) transmission, the BER of 2-Gbaud independent dual-SSB vector signal (1-Gbuad RSB and 1-Gbaud LSB) can reach the hard-decision forward error correction (HD-FEC) threshold of 3.8 × 10-3 when the input optical power into PD is -20 dBm. For 1-km and 2-km weak turbulence free-space optical (FSO) channel transmission, the BER of 2-Gbaud independent dual-SSB vector signal can reach the HD-FEC threshold when the input optical power into PD is -18.8 and -17 dBm, respectively. For 1-km weak turbulence FSO channel transmission, the BER of 4-, 8-, and 16-Gbuad independent dual-SSB vector signal can reach the HD-FEC threshold when the input optical power into PD is -17.8, -16, and -15 dBm, respectively.

Porous Supramolecular Assembly of Pentiptycene-Containing Gold(I) Complexes: Persistent Excited-State Aurophilicity and Inclusion-Induced Emission Enhancement.

We report herein a porous supramolecular framework formed by a linear mononuclear Au(I) complex (1) via the tongue-and-groove-like joinery between the pentiptycene U-cavities (grooves) and the rod-shaped π-conjugated backbone and alkyl chains (tongues) with the assistance of C-H···π and aurophilic interactions. The framework contains distorted tetrahedral Au4 units, which undergo stepwise and persistent photoinduced Au(I)-Au(I) bond shortening (excited-state aurophilicity), leading to multicolored luminescence photochromism. The one-dimensional pore channels could accommodate different solvates and guests, and the guest inclusion-induced luminescence enhancement (up to 300%) and/or vapochromism are characterized. A correlation between the aurophilic bonding and the luminescence activity is uncovered by TDDFT calculations. Isostructural derivatives 2 and 3 corroborate both the robustness of the porous supramolecular assembly and the mechanisms of the stimulation-induced luminescence properties of 1. This work demonstrates the cooperation of aurophilicity and structural porosity and adaptability in achieving novel supramolecular photochemical properties.

Chaotic RNA and DNA for security OFDM-WDM-PON and dynamic key agreement.

A chaotic ribonucleic acid (RNA) and deoxyribonucleic acid (DNA) encryption scheme is firstly proposed for security OFDM-WDM-PON in this paper. We adopt a dynamic key agreement based on the messenger RNA (mRNA) codebook to distribute the key, and the security and randomness of this key are enhanced by a pre-sharing key parameter set instead of transmission of a key directly. Also, the security key can be dynamically updated in real-time according to the needs of the users. The real (I) and imaginary (Q) parts of the QAM symbol matrix after modulation are encrypted by the correspondence between transfer RNA (tRNA) and amino acids and the selection mapping of DNA base complementary rules. Also, we add cubic permutation to ensure all data security encryption. The encrypted signals of 35.29 Gb/s on different wavelength channels are successfully demonstrated over a 25-km standard single-mode fiber (SSMF) and a back-to-back (BTB) system. It is proved that the proposed security OFDM-WDM-PON encryption scheme is compatible with the traditional WDM system, which can make full use of bandwidth resources and enhance the security with a large key space.

Compressive sensing chaotic encryption algorithms for OFDM-PON data transmission.

In this paper, we propose chaotic compressive sensing (CS) encryption algorithms for orthogonal frequency division multiplexing passive optical network (OFDM-PON), aiming at compressing the transmitted data and enhancing the security of data transmission. Bitstream transmission using CS directly is restricted due to its inability to satisfy the sparsity in neither time nor frequency domain. While the sparsity of the transmitted data can be constructed when transmitting the multimedia. A sensor can be then used to identify whether the data is multimedia. If it is, the CS technique is used, and the sensor's result is set as side information inserted into the pilot and transmitted to the terminal simultaneously. For encryption processing, a 2-dimensional logistic-sine-coupling map (2D-LSCM) is used to generate pseudo-random numbers to construct the first row of a measurement matrix to encrypt the system. Four transform formats are then applied to generate the sparsity of the transmitted data. Due to the restriction of data transmission in the physical layer, the discrete cosine transform (DCT) is chosen to conduct the CS technique. Four approximation algorithms are also proposed to optimize the performance of compressing the length of bits. We find that 'Round + Set negative to 0' shows the best performance. The combination of this chaotic CS encryption technique with the OFDM-PON systems saves the bandwidth and improves the security.

Ultra-Wideband Positioning Sensor with Application to an Autonomous Ultraviolet-C Disinfection Vehicle.

Due to the COVID-19 virus being highly transmittable, frequently cleaning and disinfecting facilities is common guidance in public places. However, the more often the environment is cleaned, the higher the risk of cleaning staff getting infected. Therefore, strong demand for sanitizing areas in automatic modes is undoubtedly expected. In this paper, an autonomous disinfection vehicle with an Ultraviolet-C (UVC) lamp is designed and implemented using an ultra-wideband (UWB) positioning sensor. The UVC dose for 90% inactivation of the reproductive ability of COVID-19 is 41.7 J/m2, which a 40 W UVC lamp can achieve within a 1.6 m distance for an exposure time of 30 s. With this UVC lamp, the disinfection vehicle can effectively sterilize in various scenarios. In addition, the high-accuracy UWB positioning system, with the time difference of arrival (TDOA) algorithm, is also studied for autonomous vehicle navigation in indoor environments. The number of UWB tags that use a synchronization protocol between UWB anchors can be unlimited. Moreover, this proposed Gradient Descent (GD), which uses Taylor method, is a high-efficient algorithm for finding the optimal position for real-time computation due to its low error and short calculating time. The generalized traversal path planning procedure, with the edge searching method, is presented to improve the efficiency of autonomous navigation. The average error of the practical navigation demonstrated in the meeting room is 0.10 m. The scalability of the designed system to different application scenarios is also discussed and experimentally demonstrated. Hence, the usefulness of the proposed UWB sensor applied to UVC disinfection vehicles to prevent COVID-19 infection is verified by employing it to sterilize indoor environments without human operation.

PAPR and security in OFDM-PON via optimum block dividing with dynamic key and 2D-LASM.

An approach for peak-to-average-power ratio (PAPR) reduction and security improvement in an orthogonal frequency division multiplexing passive optical network (OFDM-PON) is proposed by using optimum block dividing with 2-dimensional logistic adjusted sine map (2D-LASM) and dynamic key assignment technique. One frame of OFDM signal can be regarded as a symbol matrix. All the divisors of length and width of matrix can be calculated out, and each divisor of length and width can be arbitrarily combined to divide the matrix into blocks. A 4D hyperchaotic system is applied to generate a cipher book for 2D-LASM. And we assign different dynamic key groups from the cipher book for 2D-LASM to encrypt different block dividing situations. Different encrypted divisor blocks can obtain different values of PAPR. The optimum dividing situation is obtained by calculating out the minimum value of PAPR (VPAPR). The values of optimum encryption signal (VPAPR-op), the original signal (VPAPR-or) and the optimization ratio (η) are gradually equal to 146, 269 and 1.82, respectively, with the number of quadrature amplitude modulation (QAM) symbols in each subcarrier increasing. Simulating 1000 sets of 120×200 QAM symbols, the distribution of η approximately meets the Rayleigh distribution and its central distribution is around 1.7. The processing time decreases with the number of QAM symbols increasing in each block. The performance of PAPR reduction is more than 3 dB between the secure optimum signal and the original signal. In addition, the hyper-threading technique with two algorithms is applied to improve the performance of the encryption method. They promote the processing time by 38.6% and 50%, respectively. Finally, a 22.06 Gb/s optimum encryption OFDM signal transmits through a back-to-back (BTB) system and a 25-km standard single mode fiber (SSMF). These experimental results verify that the proposed approach is a promising candidate for solving both of PAPR reduction and security improvement in access network systems.

Security enhancement for OFDM-PON using Brownian motion and chaos in cell.

We propose a novel security enhancement technique for a physical layer secure orthogonal frequency division multiplexed passive optical network (OFDM-PON) based on three-dimensional Brownian motion and chaos in cell (3DBCC). This method confuses an OFDM symbol via transforming it into a 3D symbol matrix and a 3D cell matrix with different size lengths. Different dividing-confusion rules then generate different complementary cumulative distribution functions (CCDFs) of peak-to-average power ratio (PAPR). And we can pre-estimate bit error rate (BER) performance by calculating the CCDF values. We also find that the processing time decreases with the matrix's side length decreasing simultaneously. A new weighted comprehensive value (Qw) is further used to evaluate the overall performance between the processing time and the BER. Finally, an experiment successfully demonstrates a physical layer secure OFDM signal transmission with 22.06-Gb/s data rate over a 25.4-km standard single mode fiber (SSMF). These results indicate that cell (53) has the weighted optimum overall performance, which verifies that the proposed encryption technique is promising for building a physical layer security enhanced OFDM-PON system with a low processing time delay and a good BER for future access network systems.

Fluorescent Probe Encapsulated in Avidin Protein to Eliminate Nonspecific Fluorescence and Increase Detection Sensitivity in Blood Serum.

Quantitative detection of trace amounts of a biomarker in protein rich human blood plasma using fluorescent probes is a great challenge as the real signal is usually obscured by nonspecific fluorescence. This problem occurs because most of the fluorescent dyes bind very tightly with blood proteins to produce a large fluorescence increase, resulting in overestimation of the biomarker concentrations and false positive diagnosis. In this paper, we report that biotinylated fluorescent probes encapsulated in avidin protein can generate very specific fluorescence in blood serum by blocking out nonspecific dye-protein interactions. We applied our novel probe design to detect two different types of biomolecules, hydrogen sulfide and nitroreductase. Our Avidin conjugated probes achieved quantitative analyte detection in blood serum; whereas concentrations were overestimated up to 320-fold when bare fluorescent probes were employed. As compared to conventional approaches where fluorescent probes are encapsulated into polymers and nanoparticles, our simple approach successfully overcomes many key issues such as dye leakage, long preparation steps, inconsistent dye-host ratios, difficulty in constructing in situ in a complex medium, and limited application to detect only small metabolites.

Fluorescent Probe Encapsulated in SNAP-Tag Protein Cavity To Eliminate Nonspecific Fluorescence and Increase Detection Sensitivity.

Despite the promising improvements made recently on fluorescence probes for the detection of enzymes and reactive small molecules, two fundamental problems remain: weaker fluorescence of many dyes in aqueous buffers and strong nonspecific signals in samples containing high protein levels. In this paper, we introduce a novel fluorescent probe encapsulated in protein cavity (FPEPC) concept as demonstrated by SNAP-tag protein and three environment-sensitive fluorescence probes to overcome these two problems. The probes were constructed by following the current probe design for enzymes and reactive small molecules but with an additional benzylguanine moiety for selective SNAP-tag conjugation. The SNAP-tag conjugated probes achieved quantitative nitroreductase and hydrogen sulfide detection in blood plasma, whereas analyte concentrations were overestimated up to 700-fold when bare fluorescent probes were employed for detection. Furthermore, detection sensitivity was increased dramatically, as our probes displayed 390-fold fluorescence enhancement upon SNAP-tag conjugation, in stark contrast to the weak fluorescence of the free probes in aqueous solutions. Compared with the conventional approaches where fluorescent probes are encapsulated into polymers and nanoparticles, our simple and general approach successfully overcame many key issues such as dye leakage, long preparation steps, inconsistent dye-host ratios, difficulty in constructing in situ in a complex medium, and limited application to detect only small metabolites.

Photonic microwave waveforms generation based on time-domain processing.

A new photonic approach of microwave waveform generator based on time-domain synthesis is proposed and experimentally demonstrated, in which two single-drive Mach-Zehnder modulators biased at quadrature point are severed as optical pulse carvers and various microwave waveforms can be generated by carving and overlapping optical field envelopes. The theoretical analysis and simulation are developed. In experiment, a square waveform with 50% duty cycle, triangular waveform with full duty cycle, and sawtooth (or reversed-sawtooth) waveform with 50% duty cycle are generated. Furthermore, a frequency doubling sawtooth (or reversed-sawtooth) waveform with full duty cycle is also obtained.

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.

Earthquake-enhanced dissolved carbon cycles in ultra-deep ocean sediments.

Hadal trenches are unique geological and ecological systems located along subduction zones. Earthquake-triggered turbidites act as efficient transport pathways of organic carbon (OC), yet remineralization and transformation of OC in these systems are not comprehensively understood. Here we measure concentrations and stable- and radiocarbon isotope signatures of dissolved organic and inorganic carbon (DOC, DIC) in the subsurface sediment interstitial water along the Japan Trench axis collected during the IODP Expedition 386. We find accumulation and aging of DOC and DIC in the subsurface sediments, which we interpret as enhanced production of labile dissolved carbon owing to earthquake-triggered turbidites, which supports intensive microbial methanogenesis in the trench sediments. The residual dissolved carbon accumulates in deep subsurface sediments and may continue to fuel the deep biosphere. Tectonic events can therefore enhance carbon accumulation and stimulate carbon transformation in plate convergent trench systems, which may accelerate carbon export into the subduction zones.

Repercussions of clinical waste co-incineration in municipal solid waste incinerator during COVID-19 pandemic.

During coronavirus disease 2019 pandemic, the exponential increase in clinical waste (CW) generation has caused immense burden to CW treatment facilities. Co-incineration of CW in municipal solid waste incinerator (MSWI) is an emergency treatment method. A material flow model was developed to estimate the change in feedstock characteristics and resulting acid gas emission under different CW co-incineration ratios. The ash contents and lower heating values of the feedstocks, as well as HCl concentrations in flue gas showed an upward trend. Subsequently, 72 incineration residue samples were collected from a MSWI performing co-incineration (CW ratio <10 wt%) in Wuhan city, China, followed by 20 incineration residues samples from waste that were not co-incineration. The results showed that the contents of major elements and non-volatile heavy metals in the air pollution control residues increased during co-incineration but were within the reported ranges, whereas those in the bottom ashes revealed no significant changes. The impact of CW co-incineration at a ratio <10 wt% on the distribution of elements in the incineration residues was not significant. However, increase in alkali metals and HCl in flue gas may cause potential boiler corrosion. These results provide valuable insights into pollution control in MSWI during pandemic.