# Entropy-Based Algorithms for Signal Processing
### Performance Improvement  
Compound fault diagnosis is challenging due to the complexity, diversity, and non-stationary characteristics of complex mechanical faults. In the contribution by [PERSON] and [PERSON] [5], \"Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation,\" a novel compound fault separation method based on singular negentropy difference spectrum (SNDS) and integrated fast spectral correlation (IFSC) is proposed. Firstly, the original signal was de-noised by SNDS, which improved the noise reduction effect of the singular difference spectrum by introducing negative entropy. Secondly, the de-noised signal was analyzed by a fast spectral correlation. Finally, IFSC took the fourth-order energy as the index to determine the resonance band and separate the fault features of different single faults. Their proposed method was applied to analyze the simulated compound signals and the experimental vibration signals. Their results show that the proposed method has an excellent performance in the separation of rolling bearing composite faults.  
With the wide applications of three-dimensional (3D) meshes in intelligent manufacturing, digital animation, virtual reality, digital cities, and other fields, more processing techniques are being developed for 3D meshes. These techniques, including watermarking, compression, and simplification, which will inevitably lead to various distortions. Therefore, how to evaluate the visual quality of the 3D mesh is becoming a crucial problem, and it is necessary to design practical tools for blind 3D mesh quality assessment. In the contribution by [PERSON] et al. [6], \"Blind Mesh Assessment Based on Graph Spectral Entropy and Spatial Features,\" authors propose a new blind mesh quality assessment method based on graph spectral entropy and spatial features, called as BMQA-GSES. The 3D mesh can be represented as graph signal. In the graph spectral domain, the Gaussian curvature signal of the 3D mesh is firstly converted with graph Fourier transform (GFT). Then the smoothness and information entropy of amplitude features are extracted to evaluate the distortion. In the spatial domain, four well-performing spatial features are combined to describe the concave and convex information and structural information of 3D meshes. All the extracted features are fused by the random forest regression to predict the objective quality score of the 3D mesh. Based on their assumption, experiments were performed successfully on public databases. The obtained results show that the proposed BMQA-GSES method provides a good correlation with human visual perception and competitive scores compared to state-of-art quality assessment methods.  
A continuous path performed by the hand in a period of time is considered for gesture recognition. Dynamic gesture recognition is a complex topic since it spans from the conventional method of separating the hand from the surrounding environment to searching for the fingers and palm. The contribution by [PERSON] and [PERSON] [7], \"Recognition of a Single Dynamic Gesture with the Segmentation Technique HS-ab and Principle Components Analysis (PCA),\" proposes a strategy of hand recognition using a PC webcam, a segmentation technique, and pre-processing of images to reduce noise and a classifier such as principal components analysis for the detection and tracking of the hand of the user. Their results show that the segmentation technique HS-ab and the method PCA are robust in the execution of the system. However, there are various conditions such as illumination, speed, and precision of the movements. For this reason, suitable extraction and classification of featuresallow the gesture's location. The system was tested with a database of training images and had a 94.74% accuracy.