This correspondence presents a construction of quasicyclic (QC) low-density parity-check (LDPC) codes based on a special type of combinatorial designs known as block disjoint difference families (BDDFs). The proposed construction of QC-LDPC codes gives parity-check matrices with column weight three and Tanner graphs having a girth lower-bounded by 6. The proposed QC-LDPC codes provide an excellent performance with iterative decoding over an additive white Gaussian-noise (AWGN) channel. Performance analysis shows that the proposed short and moderate length QC-LDPC codes perform as well as their competitors in the lower signal-to-noise ratio (SNR) region but outperform in the higher SNR region. Also, the codes constructed are quasicyclic in nature, so the encoding can be done with simple shift-register circuits with linear complexity.

Currently both language and images are essential to the texts we use while language and images can combine for literacy education. In this research work, an application is developed to translate the text integrated to images for visual literacy. Further, several approaches for image-to-text multilingual translator are reviewed in detail. By overcoming the gaps, which are identified by thorough review of the literature, an improved methodology is proposed. As a result, the development of application goes through four major phases including: capturing, extraction, recognition and translation. Moreover, Optical Character Recognition algorithm is particularly used for character extraction and recognition with high accuracy under different environmental circumstances. It translates text just by capturing an image with user smart phone camera and translation instantly appears on user's mobile screen in language selected by the user. The proposed solution may particularly be helpful in literacy education, for learning different languages and possibly can work as visitors' assistant.

Multimedia technology is growing day by day and contributing towards enormous amount of video data especially in the area of security surveillance. The browsing through such a large collection of videos is a challenging and time-consuming task. Despite the advancement in technology automatic browsing, retrieval, manipulation and analysis of large videos are still far behind. In this paper a fully automatic human-centric system for video summarization is proposed. In most of the surveillance applications, human motion is of great interest. In proposed system the moving parts in the video are detected using background subtraction, and blobs are extracted from the binary image. Human detection is done through Histogram of Oriented Gradient (HOG) using Support Vector Machine (SVM) classifier. Then, motion of humans is tracked through consecutive frames using Kalman filter, and trajectory of each person is extracted. The analysis of trajectory leads to a meaningful summary which covers only important parts of video. One can also mark region of interest to be included in the summary. Experimental results show the proposed system reduces long video into meaningful summary and saves a lot of time and cost in terms of storage, indexing and browsing effort.