Convolutional Neural Networks (CNNs) have recently been proposed as a solution in texture and material classification in computer vision. However, inside CNNs, the internal layers of pooling often cause a loss of information and, therefore, is detrimental to learning the architecture. Moreover, when considering images with repetitive and essential patterns, the loss of this information affects the performance of subsequent stages, such as feature extraction and analysis. In this paper, to solve this problem, we propose a classification system with a new pooling method called Discrete Wavelet Transform Pooling (DWTP). This method is based on the image decomposition into sub-bands, in which the first level sub-band is considered as its output. The objective is to obtain approximation and detail information. As a result, this information can be concatenated in different combinations. In addition, wavelet pooling uses wavelets to reduce the size of the feature map. Combining these methods provides acceptable classification performance for three databases (CIFAR-10, DTD, and FMD). We argue that this helps eliminate overfitting and that the learning graphs reflect that the datasets show learning generalization. Therefore, our results indicate that our method based on wavelet analysis is feasible for texture and material classification. Moreover, in some cases, it outperforms traditional methods.

This paper proposes a first approach based on wavelet analysis inside image processing for object detection with a repetitive pattern and binary classification in the image plane, in particular for navigation in simulated environments. To date, it has become common to use algorithms based on convolutional neural networks (CNNs) to process images obtained from the on-board camera of unmanned aerial vehicles (UAVs) in the spatial domain, being useful in detection and classification tasks. CNN architecture can receive images without pre-processing, as input in the training stage. This advantage allows us to extract the characteristic features of the image. Nevertheless, in this work, we argue that characteristics at different frequencies, low and high, also affect the performance of CNN during training. Thus, we propose a CNN architecture complemented by the 2D discrete wavelet transform, which is a feature extraction method. Wavelet analysis allows us to use time-frequency information through a multiresolution analysis. Therefore, we have investigated the combination of multiresolution analysis, via wavelets, in conjunction with CNN architectures, to use the information in the wavelet domain as input to the training stage. The information improves the learning capacity, eliminates the overfitting, and achieves a better efficiency in the detection of a target. Also, our learning model was evaluated in the aerial navigation of a drone.

This study presents a method based on elementary logic and arithmetic operations to enhance the cryptographic properties of Substitution Boxes (S-Boxes). S-Boxes are a crucial component of cryptosystems, as they apply the confusion principle to information before it is encrypted, making them vital for ensuring the security of sensitive information transmitted through insecure channels. The proposed method employs bitwise XOR, Modular Addition, and Circular Shift operations, which are applied to selected S-Boxes, resulting in numerous S-Box variants that have no fixed points or reverse fixed points. We found that some of these variants can increase nonlinearity when using modular addition or circular shift operations and are therefore more suitable for use in cryptosystems. Our study contributes to the understanding of how S-Boxes can be enhanced by elementary logic and arithmetic operations. We recommend using the proposed method with the bitwise XOR operation when the S-Box has high nonlinearity (112) but requires removing fixed points and reverse fixed points. Otherwise, first use modular addition or circular shift operations to increase nonlinearity.