Md. Akter Hussain

Retinal vascular branch and crossover points are unique features for each individual that can be used as a reliable biometric for personal authentication and can be used for information retrieval and security application. In this work, a novel biometric authentication scheme is proposed based on the retinal vascular network features. We apply an automatic technique to detect and identify retinal vascular branch and crossover points. These branch and crossover points are mapped from prominent blood vessels in the image. For this, a novel vessel width measurement method is applied and vessels more than certain widths are selected. Based on these vessel segments their corresponding branch and crossover points are identified. Invariant features are constructed through Geometric Hashing of the detected branch and crossover points. We consider the crossover points for modelling a basis pair and all other points together for locations in the hash table entries. Thus, the models are invariant to rotation, translation and scaling. For each person, the system is trained with the models to accept or reject a claimed identity. The initial results show that the proposed method has achieved 100% detection accuracy which is highly potential for reliable person identification.

The most challenging part of a web crawler is to download contents at the fastest rate to utilize
bandwidth and processing of the downloaded data so that it will never starve the downloader. Our
implemented scalable web crawling system, named as WEBTracker has been designed to meet this
challenge. It can be used very efficiently in the distributed environment to maximize downloading.
WEBTracker has a Central Crawler Server and it administers all the crawler nodes. At each
crawler node, Crawler Manager runs the downloader and manages the downloaded contents.
Central Crawler Server and its Crawler Managers are members of the Distributed File System
which ensures synchronized distributed operations of the system. In this paper, we have only
concentrated on the architecture of a web crawling node which is owned by the Crawler Manager.
We have shown that our implemented crawler architecture makes efficient use of allocated
bandwidth, keeps the processor less busy for the processing of downloaded contents and makes
efficient use of the run time memory.

Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. For predicting of share price using ANN, there are two modules, one is training session and other is predicting price based on previously trained data. We used Backpropagation algorithm for training session and Multilayer Feedforward network as a network model for predicting price. In this paper, we introduce a method which can predict share market price using Backpropagation algorithm and Multilayer Feedforward network.

This paper presents methodologies involved in text normalization and diphone preparation for Bangla Text to Speech (TTS) synthesis. A Concatenation based TTS system comprises  basically two modules-  one is natural language processing and the other is Digital Signal Processing (DSP). Natural language processing deals with converting text to its pronounceable  form, called Text Normalization and the diphone selection method based on the normalized text is called Grapheme to Phoneme (G2P) conversion.  Text normalization  issues addressed in this paper include tokenization, conjuncts, null modified characters, numerical words, abbreviations  and  acronyms.  Issues related with diphone preparation include diphone categorization, corpus preparation,  diphone  labeling and  diphone selection. Appropriate rules and algorithms are proposed to tackle all the above mentioned issues.  We developed a  speech synthesizer for Bangla using diphone based concatenative approach which is demonstrated to produce much natural sounding synthetic speech.

This paper presents methodologies involved in diphone preparation for Bangla text to speech synthesis. A concatenation based synthesis system comprises basically two modules- one is natural language processing and other is digital signal processing (DSP). Natural language processing implies converting text to its pronounceable text, called text normalization and the diphone selection method based on the normalized text is called Graphene to Phoneme (G2P) conversion. We developed a speech synthesizer for Bangla using diphone based concatenative approach. Diphone preparation, labeling and selection techniques are described in this paper.