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The Open Circuit (OC) faults occurring in switches of Multilevel Converters (MLC) may lead to undesirable operation of the converter. Therefore, fault detection and its localization in minimum time are necessary. This paper focuses on the fast fault detection algorithm based on the two samples technique and the fault localization algorithm using the Entropy of Wavelet Packets (EWP) as a feature. The EWP feature is used to classify and localize the OC faults in Insulated Gate Bipolar Transistors (IGBTs) of three-phase, three-level inverter using Support Vector Machine (SVM) based fault classification algorithm. The proposed technique can detect the fault in single IGBT and multiple IGBTs in a lesser time range of microseconds to 0.33 ms. It gives better performance and accuracy (99.70%) than previously proposed SVM algorithms, as the EWP-based feature extraction process used in this paper is simple and accurate with a less computational burden. [Display omitted] • An observer-based algorithm is proposed using two samples-based technique. • The Entropy of Wavelet Packets (EWP-SVM) technique is used for fault diagnosis. • The proposed algorithm can detect the faults in a single IGBT and multiple IGBTs. • The technique is faster and accurate than techniques available in the literature. • The comparison of detection time of different techniques is also discussed. [ABSTRACT FROM AUTHOR]

1 Creating a dataset for estimating depth from target foreground and background scene variations. 2 Creation of relationships between depth of person, foreground and background scene field. 3 Dealing with perspective errors in depth predictions while capturing still photographs of person along camera axial line. This paper proposes a new technique to achieve a person's depth from a single image by considering the target foreground and background scene variations in extreme weather conditions within 40 meters range. For this purpose series of images are captured on each person at successive intervals. The height, distance, foreground, and background features are extracted using an object detection deep learning framework. The obtained features are then subsequently trained by the Gradient Booster Regressor to predict the depth information. Furthermore, the algorithm is tested on various images and is validated with ground truth depth data. The findings presented in this paper attest to the reliability of the methodology used for depth estimation. [Display omitted] [ABSTRACT FROM AUTHOR]

• The novelty of the work presented in this paper is that a information fusion algorithm of the maximum multi-modal weight decision is proposed. The emotion recognition scheme is developed by using the multi-modal information fusion algorithm. • Another major contribution of this work is the innovative conversion of traditional EEG data into corresponding EEG topographic maps for the emotion recognition. • Besides, this paper builds a multi-scale feature extraction network model for the facial expression recognition. In view of the low accuracy of the traditional emotion recognition methods based on facial expressions, an emotion recognition method based on maximum weight multi-modal information fusion of electroencephalographs (EEGs) and facial expression information is proposed in this paper. First, the induced emotional EEG data is converted into the corresponding EEG topographic map data and sent to the convolutional network for training and outputting decision information. Second, the illumination compensation method is utilized to filter the noise of the face image data. Then, the face image data is trained in the multi-scale feature extraction network, and the decision information is output. Finally, aiming at the decision-level information fusion, a weighted fusion method is proposed in this paper for emotion recognition. Experimental tests show that the recognition accuracy of the multi-scale feature extraction network on the CK+ data set and Fer2013 data reached 94.4% and 72%, respectively. Simultaneously, the multi-modal information fusion method achieves 92.6% accuracy in emotion recognition. [Display omitted] The novelty of the work presented in this paper is that a fusion method of maximum weight decision information is proposed. Emotion recognition is realized by the weighted fusion of EEG information and facial expression information. Another major contribution of this work is the innovative conversion of traditional EEG data into corresponding state EEG topographic maps for processing and analysis. Besides, this paper builds a multi-scale feature extraction network for emotion recognition. The graphical abstract of this paper is as below. [ABSTRACT FROM AUTHOR]

Detecting the linear features in an image is a key technology for different applications. In this paper, a simple and effective algorithm based on the hit-or-miss transform is proposed. To detect linear features with different directions, multi-structuring elements corresponding to different directions are constructed. To detect linear features with different widths, a multi-scale extension of the constructed multi-structuring elements is used. Then, the grey-level hit-or-miss transform that utilizes the constructed multi-scales of multi-structuring elements could effectively extract all of the possible linear features without thresholding. Therefore, after refining the extracted linear feature regions using three simple steps, the final linear features could be effectively detected. Experimental results on different images from different applications show that the proposed algorithm performs well for detecting linear features with different widths, different grey distributions and noises. [ABSTRACT FROM AUTHOR]

Pose variation is the major factor significantly affecting recognition efficiency in the field of face recognition. In this paper, we propose a novel algorithm for pose-variant face recognition. We first use the affine-invariant multi-scale autoconvolution (MSA) transformation to extract pose-invariant features. Following this, we use principal component analysis (PCA) to decorrelate the feature sets and reduce the number of required MSA dimensions. The PCA components with sufficiently large corresponding eigenvalues are then passed to the k nearest classifier. Compared with other pose-variant face recognition techniques, our proposed algorithm exhibits a superior ability to pose changes, illumination changes, and different face databases, moreover records a higher recognition rate. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to propose and implement an experimental fully automatic face recognition system which will be used to annotate photographs during insertion into a database. Its main strength is to successfully process photos of a great number of different individuals taken in a totally uncontrolled environment. The system is available for research purposes for free. It uses our previously proposed SIFT based Kepenekci approach for the face recognition, because it outperforms a number of efficient face recognition approaches on three large standard corpora (namely FERET, AR and LFW). The next goal is proposing a new corpus creation algorithm that extracts the faces from the database and creates a facial corpus. We show that this algorithm is beneficial in a preprocessing step of our system in order to create good quality face models. We further compare the performance of our SIFT based Kepenekci approach with the original Kepenekci method on the created corpus. This comparison proves that our approach significantly outperforms the original one. The last goal is to propose two novel supervised confidence measure methods based on a posterior class probability and a multi-layer perceptron to identify incorrectly recognized faces. These faces are then removed from the recognition results. We experimentally validated that the proposed confidence measures are very efficient and thus suitable for our task. [ABSTRACT FROM AUTHOR]

An improved image magnification algorithm for gray and color images is presented in this paper to meet the challenge of preserving high-frequency components of an image, including both image edges and texture structures. In the proposed algorithm, a new edge detection method that uses the well-known Otsu automatic optimum thresholding is proposed to distinguish strong edge pixels. The parameters of the original directional cubic convolution interpolation algorithm, which were selected based on training, were eliminated. As a result, our algorithm achieves more accurate edge detection, better interpolation results, and less computational complexity. Simulation results demonstrate that the improved algorithm can reconstruct the magnified image, preserve edges and textures simultaneously, and reduce common interpolation artifacts. Furthermore, it generates higher visual quality of the magnified images and achieves higher peak signal-to-noise ratio, structural similarity, and feature similarity compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In pattern recognition, feature extraction techniques have been widely employed to reduce the high dimensionality of data. In this paper, we propose a novel algorithm called fuzzy local discriminant embedding (FLDE) based on the unsupervised discriminant projection criterion and fuzzy set theory for image feature extraction and recognition. In the proposed method, a membership degree matrix is firstly calculated using the fuzzy k -nearest neighbor (FKNN) algorithm, and then the membership degree and the label information are incorporated into the definition of the weighted matrices to get the fuzzy local scatter and fuzzy nonlocal scatter. After characterizing the fuzzy nonlocal scatter and the fuzzy local scatter, a concise feature extraction criterion is derived via maximizing the ratio between them. Experimental results on the ORL, FERET, and CMU PIE face databases show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel reduced-reference stereoscopic image quality assessment (RR-SIQA) algorithm is proposed by means of an unconventional use of watermarking technique. Watermarking techniques are usually employed for authenticity verification and copyright protection. Here, watermarking technique is adopted to provide a new approach for RR-SIQA. Firstly, the features of image are extracted in reorganized discrete cosine transform domain, and then embedded into the stereoscopic image as invisible hidden information. In order to improve the reliability of the watermarking, some channel coding techniques are applied before the process of embedding watermark. At the receiver, the watermark can be decoded and used to measure the quality of the distorted stereoscopic image. The proposed algorithm overcomes the limitations of other existing methods that require an auxiliary channel. Experimental results illustrate that the proposed algorithm has a good consistency with subjective quality scores, and can reflect the visual perception of stereoscopic image effectively. [ABSTRACT FROM AUTHOR]

As an essential subproblem in the face recognition field, occluded face recognition has received considerable attention in recent years. However, satisfactory recognition accuracy for occluded faces has not been achieved. Therefore, in order to solve this problem, this paper proposes a convolutional neural network that has multidimensional serial feature extraction modules for occluded faces and uses the deep learning method to improve the recognition rate. In order to improve the expression ability of the network, the method extracts features from two dimensions — space and channel — and built a multidimensional serial feature extraction module. By "multiscale" and "dependence" processing features, the generalization ability was significantly improved. Through the experimental test, the recognition accuracy of the multidimensional feature network (MFNet) reached 90.35%. This accuracy is 17.89% (72.36%) better than that of the traditional algorithm. Meanwhile, compared with other convolutional neural networks, this network has different degrees of improvement, which are 0.68% (ArcFace), 2.14% (Visual Geometry Group Face), and 4.83% (DeepID3). [ABSTRACT FROM AUTHOR]

• The network can directly learn end-to-end mapping between low-resolution images and high-resolution images, and it can use convolution and deconvolution cascade coding to eliminate noise. • Apply dilated convolution layer in the parts, so that small convolution kernel can obtain a large receptive field, which can improve the reconstruction effect and reduce the calculation amount. • Use residual learning method to quickly optimize the network. By adding multiple connections to the network, speed up the training speed and improve the performance. The existing image processing methods had aimed at the problems of blurred image reconstruction, large noise, and poor visual perception. The improved image super-resolution algorithm based on mixed deep convolutional networks is proposed in the paper. Firstly, the proposed method can shrink the low-resolution image to the specified size in upsampling phase. Secondly, it can extract features from low-resolution images. It sends the extracted initial features into the convolutional coding and decoding structure for image features. Thirdly, the feature extraction and calculation in high-dimensions are performed using dilated convolution in reconstruction layer. The high-resolution image had been reconstructed. The proposed method had been compared with state-of-arts on Set5, Set14, BSD100, and Urban100 datasets. The experimental results can show that the Peak Signal-to-Noise Ratio is increased by some ranges, and the Structural Similarity is increased by some effective percentage points. [Display omitted] [ABSTRACT FROM AUTHOR]