Sharif Digital Repository

Reflection appears as a layer that partly covers the original image. This phenomenon may cause failure in extracting information from images of reflecting objects. This work presents an automated technique for determining of reflection area as well as its severity to define reliability of extracted information. This is done by analyzing histogram and objects found in the image. We present a strong detector based on combining the results of these two procedures. We presented new reflection detection algorithm and a new method to find a good threshold value for converting any image into a binary image. The average reflection detection accuracy of the proposed algorithm is more than 95% for... 

Beenakker noticed that the peculiar band structure of Dirac fermions in two-dimensional (2D) solids allows for the specular Andreev reflection in these systems, which has no analogue in other 2D electron systems. An interesting deformation of the Dirac equation in the solid state is to tilt it, which has now been realized in materials. In this paper we report another peculiar feature of the Andreev reflection in tilted 2D Dirac cone systems. The tilt deformation of the Dirac equation is characterized by two parameters ζ=ζ(cosθ,sinθ). We show that when the tilt parameter is tuned to its "horizon value" ζ=1, irrespective of the incidence angle of electrons, the Andreev reflected hole is always... 

Voice activity detection is an important step in some speech processing systems, such as speech recognition, speech enhancement, noise estimation, speech compression ... etc. In this paper a new voice activity detection algorithm based on wavelet transform is proposed. In this algorithm we use the energy in each sub band, and by two methods we extract feature vector from these values. Experimental results demonstrate advantage over different VAD methods. ©2008 IEEE  

In this paper, we consider resonant interaction between a surface wave and sub-harmonic interfacial waves forming a two-dimensional pattern at the interface between two fluid layers. The resulting interfacial pattern changes both in time and space. An image processing technique is proposed to extract information about the evolution of the component interfacial waves in a wave flume. The technique proved to give accurate and consistent results when applied to different stages of the interaction. The experimental measurements were used to verify the theoretical predictions. The method can be used for detection of some other wave motions  

Online media play an important role in information societies by providing a convenient infrastructure for different processes. Information diffusion that is a fundamental process taking place on social and information networks has been investigated in many studies. Research on information diffusion in these networks faces two main challenges: (1) In most cases, diffusion takes place on an underlying network, which is latent and its structure is unknown. (2) This latent network is not fixed and changes over time. In this article, we investigate the diffusion network extraction (DNE) problem when the underlying network is dynamic and latent. We model the diffusion behavior (existence... 

This paper combines the cryptanalysis of RC4 and Data mining algorithm. It analyzes RC4 by Data mining algorithm (J48) for the first time and discloses more vulnerabilities of RC4. The motivation for this paper is combining Artificial Intelligence and Machine learning with cryptography to decrypt cyphertext in the shortest possible time. This analysis shows that lots of numbers in RC4 during different permutations and substitutions do not change their positions and are fixed in their places. This means KSA and PRGA are bad shuffle algorithms. In this method, the information theory and Decision trees are used which are very powerful for solving hard problems and extracting information from... 

We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints. Using the selective exchange of information between a user's device and a service provider, DPFE enables the user to prevent certain sensitive information from being shared with a service provider, while allowing them to extract approved information using their model. We introduce and utilize the log-rank privacy, a novel measure to assess the effectiveness of DPFE in removing sensitive information and compare different models based on their accuracy-privacy trade-off. We then implement and evaluate the performance of DPFE on smartphones to... 

In this paper, we have proposed an efficient and effective nonlinear feature domain noise suppression algorithm, motivated by the minimum mean square error (MMSE) optimization criterion. A Multi Layer Perceptron (MLP) neural network in the log spectral domain has been employed to minimize the difference between noisy and clean speech. By using this method, as a pre-processing stage of a speech recognition system, the recognition rate in noisy environments has been improved. We extended the application ofthe system to different environments with different noises without retraining HMMmodel. We trained the feature extraction stage with a small portion of noisy data which was created by... 

We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints. Using the selective exchange of information between a user's device and a service provider, DPFE enables the user to prevent certain sensitive information from being shared with a service provider, while allowing them to extract approved information using their model. We introduce and utilize the log-rank privacy, a novel measure to assess the effectiveness of DPFE in removing sensitive information and compare different models based on their accuracy-privacy trade-off. We then implement and evaluate the performance of DPFE on smartphones to... 

Glioma is one of the most frequent primary brain tumors in adults that arise from glial cells. Automatic and accurate segmentation of glioma is critical for detecting all parts of tumor and its surrounding tissues in cancer detection and surgical planning. In this paper, we present a reliable classification framework for detection and segmentation of abnormal tissues including brain glioma tumor portions such as edemas and tumor core. This framework learns weighted features extracted from the 3D cubic neighborhoods regarding to gray-level differences that indicate the spatial relationships among voxels. In addition to intensity values in each slice, we consider sets of three consecutive... 

The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that are happening globally i.e., sports, inflation, protest, explosion, and sexual assault, etc. in regional (local) languages on social media. Extraction and classification of events from multilingual data have become bottlenecks because of resource lacking. In this research paper, we presented the event classification task for the Urdu language text existing on social media and the news channels by... 

Mean field independent component analysis (MF-ICA) along with other chemometric techniques was proposed for obtaining more information from multi-component gas chromatographic-mass spectrometric (GC-MS) signals of essential oils (mandarin and lemon as examples). Using these techniques, some fundamental problems during the GC-MS analysis of essential oils such as varying baseline, presence of different types of noise and co-elution have been solved. The parameters affecting MF-ICA algorithm were screened using a 25 factorial design. The optimum conditions for MF-ICA algorithm were followed by deconvolution of complex GC-MS peak clusters. The number of independent components (ICs) (chemical...