# Entropy-Based Algorithms for Signal Processing
### Models  
In the contribution by [PERSON] et al. [1], \"Two-Dimensional Permutation Vectors' Code for Optical Code Division Multiple Access Systems,\" authors presented a new algorithm to generate two-dimensional permutation vectors' code for an incoherent optical code division multiple access (OCDMA) system to suppress multiple access interference and system complexity. The proposed code design approach is based on a wavelength-hopping time-spreading technique for code generation. All possible combinations of PV code sets were attained by employing all permutations of the vectors with the repetition of each vector weight times. Furthermore, a 2D-PV code set was constructed by combining two code sequences of the 1D-PV code. The transmitter-receiver architecture of the 2D-PV code-based WHTS OCDMA system is presented. Their results indicate that the 2D-PV code provides increased cardinality by eliminating phase-induced intensity noise effects, and multiple user data can be transmitted with a minimum likelihood of interference. Simulation results validated the proposed system for an agreeable bit error rate of \(10^{-9}\). The contribution by [PERSON] and [PERSON] [2], \"A Cross Entropy Based Deep Neural Network Model for Road Extraction from Satellite Images,\" proposes a deep convolutional neural network model with an encoder-decoder architecture to extract a road network from satellite images. The authors employ ResNet-18 and atrous spatial pyramid pooling technique to tradeoff between the extraction precision and running time. A modified cross-entropy loss function is proposed to train their deep model. A PointRend algorithm is used to recover a smooth, clear, and sharp road boundary [3]. The augmented DeepGlobe dataset is used to train their deep model, and the asynchronous training method is applied to accelerate the training process. Five satellite images covering Xiaomu village are taken as input to evaluate their model. The proposed E-Road model has fewer number of parameters and shorter training time. Their experimental results show that the E-Road outperforms other state-of-the-art deep models with 5.84% to 59.09% improvement, and can give accurate predictions for the images with a complex environment. In the contribution by [PERSON] et al. [3], \"A Multiple-Input Multiple-Output Reservoir Computing System Subject to Optoelectronic Feedbacks and Mutual Coupling,\" a multiple-input multiple-output reservoir computing (RC) system is proposed. The system is composed of multiple nonlinear nodes (Mach-Zehnder modulators) and multiple mutual-coupling loops of optoelectronic delay lines. Each input signal is added into every mutual-coupling loop to implement the simultaneous recognition of multiple route signals, which results in the signal processing speed improving improvements and the number of routes increasing. As an example, the four-route input and four-route output RC is simultaneously realized by numerical simulations. Their results show that this type of RC system can successfully recognize the four-route optical packet headers with 3-bit, 8-bit, 16-bit, and 32-bit, and four-route independent digital speeches. When the white noise is added to the signals such that the signal-to-noise ratio (SNR) of the optical packet headers and the digital speeches are 35 and 20 dB respectively, the normalized root mean square errors (NRMSEs) of the signal recognition are all close to 0.1. The word error rates (WERs) of the optical packet header recognition are 0%. The WER of digital speech recognition is 1.6%. The eight-route input and eight-route output RC is also numerically simulated. The identification of the eight-route 3-bit optical packet headers is implemented. The parallel processing of multiple-route signals and the high recognition accuracy are implemented by this proposed system.