Capacity Scaling Limits and New Advancements in Optical Transmission Systems

Optical transmission technologies have gone through several generations of development.Spectral efficiency has significant ly improved,and industry has begun to search for an answer to a basic question：What are the fundamental linear and nonlin ear signal channel limitations of the Shannon theory when there is no compensation in an optical fiber transmission system？Next-generation technologies should exceed the 100G transmis sion capability of coherent systems in order to approach the Shannon limit.Spectral efficiency first needs to be improved be fore overall transmission capability can be improved.The means to improve spectral efficiency include more complex modulation formats and channel encoding/decoding algorithms,prefiltering with multisymbol detection,optical OFDM and Ny quist WDM multicarrier technologies,and nonlinearity compen sation.With further optimization,these technologies will most likely be incorporated into beyond-100G optical transport sys tems to meet bandwidth demand.

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R2) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.

Stability evaluation of the PROSPECT model for leaf chlorophyll content retrieval

The radiative transfer model,PROSPECT,has been widely applied for retrieving leaf biochemical traits.However,little work has been conducted to evaluate the stability of the PROSPECT model with consideration of multiple factors(i.e.,spectral resolution,signal-to-noise ratio,plant growth stages,and treatments).This study aims to investigate the stability of the PROSPECT model for retrieving leaf chlorophyll(Chl)content(Cab).Leaf hemispherical reflectance and transmittance of oilseed rape were acquired at different spectral resolutions,noise levels,growth stages,and nitrogen treatments.The Chl content was also measured destructively by using a microplate spectrophotometer.The performance of the PROSPECT model was compared with a commonly used random forest(RF)model.The results showed that the prediction accuracy of PROSPECT and RF models for Cab did not produce significant differences under varied spectral resolutions ranging from 1 to 20 nm.The ranges of the relative root mean square errors(rRMSE)of the PROSPECT and RF models were 12%-13%and 11.70%-12.86%,respectively.However,the performance of both models for leaf Chl retrieval was strongly influenced by the noise level with the rRMSE of 13-15.37%and 12.04%-15.80%for PROSPECT and RF,respectively.For different growth stages,the PROSPECT model had similar prediction accuracies(rRMSE=9.26%-12.41%)to the RF model(rRMSE=9.17%-12.70%).Furthermore,the superiority of the PROSPECT model(rRMSE=10.10%-12.82%)over the RF model(rRMSE=11.81%-15.47%)was prominently observed when tested with plants growth at different nitrogen treatment levels.The results demonstrated that the PROSPECT model has a more stable performance than the RF model for all datasets in this study.