Amharic Braille image recognition into a print text is not an easy task because Amharic language has large number of characters requiring corresponding representations in the Braille system. In this paper, we propose a system for recognition of double sided Amharic Braille documents which needs identification of recto, verso and overlapping dots. We used direction field tensor for preprocessing and segmentation of dots from the background. Gradient field is used to identify a dot as recto or verso dots. Overlapping dots are identified using Braille dot attributes (centroid and area). After identification, the dots are grouped into recto and verso pages. Then, we design Braille cell encoding and Braille code translation algorithms to encode dots into a Braille code and Braille codes into a print text, respectively. With the purpose of using the same Braille cell encoding and Braille code translation algorithm, recto page is mirrored about a vertical symmetric line. Moreover, we use the concept of reflection to reverse wrongly scanned Braille documents automatically. The performance of the system is evaluated and we achieve an average dot identification accuracy of 99.3% and translation accuracy of 95.6%. 

Biometric science is one of the important applications in the pattern recognition field. There are several modalities used in the biometric applications, among these different traits we choose the iris modality. Therefore, this paper proposes a multi-biometric technique which combines the both units of the iris modality: the left and the right irises. The fusion combines the advantages of the two instances. For the both units of the iris, the segmentation is realized by a modified method and the feature extraction is done by a global approach (the Daubechies wavelets). The Support Vector Machine SVM is used to obtain scores for fusion. Then the scores obtained are normalized by Min-Max method and the fusion is performed at score level by the combination of two methods: a combination method with a classification method. The Fusion is tested using four databases which are: CASIAV4 database, SDUMLA-HMT database, MMU1, and MMU2 databases. The obtained results have confirmed that the multi-biometric systems are better than the mono-modal systems according to their performance.

There are presently about 40 special economic zones (SEZs) across the seven federating emirates of the United Arab Emirates. These SEZs include businesses technology hubs, science cities, recreational parks, and media parks. This study aims to document how the SEZs change or affect the transformation of urban expansion and land use practices in the UAE. The study aims to show the new industrial growth and expansion related with land use that emerge around the SEZs compared to before their establishment. The research is based on sequential analysis of temporal geospatial digital maps generated from archival Landsat TM in 2000 and Landsat 8 OLI of 2015. The study area is located around Alqouz and Albarsha localities situated in the western side of the city of Dubai with a vibrant SEZs. The analysis results show remarkable expansion of 23.6% of industrial and warehouse infrastructure coupled with major residential expansion of 18.6%. The findings of this research would help local authorities and corporation in planning for the future of these global businesses and local sustainability in the context of environmental planning and sustainability.

The most common malignancy observed among Indian women is the breast cancer. However, the cancer is detectable earlier by means of mammograms. Computer Aided Diagnostic (CAD) techniques are the boon to medical industry and these techniques intend to support the physicians in diagnosis. In this paper, a novel CAD system for the detection and classification of the abnormalities in the mammogram is presented. The proposed work is organized into four important phases and they are pre-processing, segmentation, feature extraction and classification. The pre-processing phase intends to remove unwanted noise and make the mammograms suitable for the next process. The segmentation phase aims to extract the areas of interest to proceed with further process. Feature extraction is the most important phase, which is meant for extracting the texture features from the area of interest. This work employs pseudo zernike moments for extracting features, owing to the noise resistance power and description ability. Finally, Support Vector Machine (SVM) is employed as the classifier, so as to distinguish between the malignant and normal mammograms. The performance of the proposed work is evaluated by several experimentations and the results are satisfactory in terms of accuracy, specificity and sensitivity.

Mismatch in speech data is one of the major reasons limiting the use of speaker recognition technology in real world applications. Extracting speaker specific features is a crucial issue in the presence of noise and distortions. Performance of speaker recognition system depends on the characteristics of extracted features. Devices used to acquire the speech as well as the surrounding conditions in which speech is collected, affects the extracted features and hence degrades the decision rates. In view of this, a feature level approach is used to analyze the effect of sensor and environment mismatch on speaker recognition performance. The goal here is to investigate the robustness of segmental features in speech data mismatch and degradation. A set of features derived from filter bank energies namely: Mel Frequency Cepstral Coefficients (MFCCs), Linear Frequency Cepstral Coefficients (LFCCs), Log Filter Bank Energies (LOGFBs) and Spectral Subband Centroids (SSCs) are used for evaluating the robustness in mismatch conditions. A novel feature extraction technique named as Normalized Dynamic Spectral Features (NDSF) is proposed to compensate the sensor and environment mismatch. A significant enhancement in recognition results is obtained with proposed feature extraction method.

Edge detection is important in image processing to aid operations such as object classification and identification amongst others. This is soley to improve interpretability of the image. Common edge detection techniques such as Sobel, Prewittt, Canny, Laplacian of Gaussian (LOG), Robertss and Zero-Crossing has attracted the attention of researchers to perform a comparative analysis on these techniques excepts fuzzy, using different type of images. Fuzzy logic based edge detection algorithms development and comparison with existing algorithm became important due to the fact that the pixels’ boundaries identifying image degs are crystal clear as expected, hence other edge detection algorithms using crisp values will be omitting some vital information pixels, this impairs the quality of the image edge detected and further application through proper interpretation. This research further extends the investigation of edge detection techniques optimality, through comparing Sobel, Prewittt, Canny, Laplacian of Gaussian (LOG), and Robertss edge detection algorithms with our proposed fuzzy based edge detection algorithm designed using MATLAB. The result indicated that the novel fuzzy based edge detection algorithm developed in this research outperforms the Canny, Sobel, Prewittt, Robertss and LOG edge detection algorithms in three different experiments with different images

Nowadays, Multimedia security [1] is a major issue. Images, video, audio, text files are losing their credibility day by day as they can be distorted or manipulated by using several tools. Ensuring the authenticity [2] and integrity of digital media is a major issue. The manipulation made by forgery tools are so smoothly done that we don’t even suspect that forgery may be involved in digital content. Multimedia data is facing several issues related to illegal distribution, duplication and manipulation of information conveyed by them. The digital watermarking [3] technique plays an important role in protecting digital content. In this paper, On the basis of their operating principles different watermarking techniques are categorized [4]. Attacks, applications and requirements [5] related to watermarking techniques are also discussed. Different watermarking techniques proposed by researchers for protecting copyrights of digital media are presented which are based on spatial and frequency domain. Frequency domain are getting much more attention due to use of wavelets which have high degree of resemblance to human visual system. In digital watermarking, secret information is embedded with original data for maintaining ownership rights of the digital content. Spatial domain watermarking techniques work over pixel characteristics and frequency domain watermarks concerned about different transformations that can be used with digital content. Imperceptibility, robustness, security, complexity and capacity are some requirements of the digital watermarking which completely depends on the algorithm used for watermarking.