Fig. 4

Intelligent multiple-OAM states sensing. a Schematic of multiple-OAM state detection empowered by MTL-DNN. The multiple-OAM state composing three weighted OAM modes is produced via a superposed phase plate. The multiple-OAM state is scattered by the disordered NLC device, and the generated speckle images are input to the MTL-DNN. The multiple-OAM states and their power spectra are recognized by two independently task-specific fully-connected layers. b The loss function on the training epochs numbers for train and test datasets of multiple-OAM states: (i) regression loss function \(\mathcal {L}_1\); (ii) classification loss function \(\mathcal {L}_2\) in semi-log axis; (iii) total loss function \(\mathcal {L}\). c The confusion matrix of 20 multiple-OAM states labeled 1-20, reporting accuracy of 100%. The right panel lists the OAM mode compositions for the different multiple-OAM states. d Four typical input and predicted spectra of multiple-OAM states. The predicted results agree reasonably well with the input spectra