Data Mining Practical Machine Learning Tools and Techniques

In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an ''unlikely'' appearance Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.

A morphable model for the synthesis of 3D faces

https://bura.brunel.ac.uk/bitstream/2438/14221/1/FullText.pdf

A survey of deep neural network architectures and their applications

A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.

/pdf/deep-learning-based-crack-damage-detection-using-1cucavd1rf.pdf

Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

Digital processing of speech signals

Identifying the tectonic settings of Archean greenstone belt-basalts: A Machine Learning approach

The study aims to learn deep features from EEG signals corresponding to the imagery of words to design a BCI(Brain computer interface) system based on human thoughts. Topological plots of time-averaged EEG signals across all the trials corresponding to the imagery of a particular word are fed as input to the designed deep neural network algorithm. Designed deep learning architecture has an amalgamation of the Two-dimensional convolutional neural network and LSTM that takes the assistance of the capabilities and assets of both neural network architectures. The proposed neural network architecture achieves admirable accuracy in identifying imagined words from the EEG-based Kara one dataset. The accuracy of the designed CNN-LSTM model with topological plots as input features was 20-25% more than chance level accuracy and comparable to the state of arts.

EEG Based Brain Computer Interface System for Decoding Covert Speech using Deep Neural Networks

This paper proposes a simple algorithm for the design of a two-channel linear phase quadrature mirror filter (QMF) bank. The design problem is formulated as a quadratic constrained least square minimisation that optimises the performance of a prototype filter (PF) and distortion transfer function of filter bank at quadrature frequency. A new method is developed for the design of a low pass prototype filter for QMF banks. For solving the given optimisation problem, a sequential quadratic programming (SQP) technique is used. Numerical examples and comparisons with several existing methods are included to show the performances and the effectiveness of the proposed method. An application of this method is considered in the area of subband coding of the images.

A simple method for designing quadrature mirror filter banks via quadratic constrained optimisation

This paper investigates the performance capability of noise estimation techniques for single - channel speech. These techniques are evaluated in presence of low SNR noises (i. e. f16, babble, white, and pink ). The noise estimation techniques have m ajor impact on the quality and intelligibil ity of denoised speech pattern. The noise estimation techniques are evaluated in frequency domain in terms of quality and intelligibility measure parameters. The Perceptual Evaluation of Speech Quality ( PESQ ) , Wei ghted Spectral Slop metric (WSS), Frequency Weighted Segmental SNR (fw - SNRseg), Speech Intelligibility Index (SII ), and output SNR parameters are used for performance evaluation of low SNR noises mixed speech patterns. The sampling frequency used for proce ssing is 8000 Hz and all algorithms are implemented in MATLAB 7.14.

Evaluation of N oise E stimation T echniques for S ingle - C hannel S peech in L ow SNR N oise E nvironment

The improvement in reverse recovery of power NPN bipolar transistor (BJT) through incorporation of “universal contact” in the base is studied in detail. It is shown that use of universal contact allows redistribution of base current in saturation from collector region where recombination lifetime is high to extrinsic base region where effective recombination lifetime is low. The reverse recovery time decreases as collector current density increases but increases as collector breakdown voltage increases. The improvement in reverse recovery is accompanied with an increase in collector–emitter voltage in the ON state. For low voltage transistors and high voltage transistors at low collector current densities, the increase is primarily due to reduction in reverse current gain. For high breakdown voltage transistors, the use of universal contact results in early onset of quasi-saturation effect and results in degradation in ON state voltage at high collector current densities.

/pdf/study-of-improved-reverse-recovery-in-power-transistor-2oy4w4bsry.pdf

Study of improved reverse recovery in power transistor incorporating universal contact

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