Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.

Varieties of environmental sources of noise and distortion can degrade the quality of the speech signal in a communication system. This research work explores the effects of these interfering sounds on speech applications and introduces a technique for reducing their influence and enhancing the acceptability and intelligibility of the speech signal. In this work, a noise reduction system using single microphone method in time domain to improve SNR of noise contaminated speech is proposed. Traditional Spectral Subtraction method has been reviewed very well and the relationship with wiener filter is also illustrated. The Spectral Subtraction method has been generalized and the focus is put on reducing noise from speech in single channel signals. Voice Activity Detector (VAD) is ignored in this proposed system, because a-priori information about the noise is assumed. The research has been conducted using Gaussian White Noise and Color Noise. The experimental result shows a remarkable improvement in SNR for the generalized version and it is noticed that the result is very much satisfactory when white noises are added but the addition of color noise produces a comparatively poor improvement report. The system has been tested with eight different datasets and on an average, 65.27% improvement in SNR (Signal to Noise Ratio) for White Noise using Generalized Spectral Subtraction Method is achieved comparing with Traditional Spectral Subtraction Method. The average improvement in SNR for Color Noise recorded is 53.31%. The Generalized Spectral Subtraction method is shown to improve the speech quality and to improve SNR as well.

Cloud computing is being considered as a revolutionary technique in the field of enterprise hardware and software design and development. As the popularity of cloud computing is increasing day by day and it’s the mostly used technique in the online and automated business system, so the security of the stored data in the cloud and the accessing method of the data through the network must be ensured. Among the various types of server and network authentication protocol Kerberos is the latest and robust network protocol. For authentication, Kerberos is badly in need of a hosted server which has to be remaining running 24*7 and the access point should be a single server (centralized authentication service). Besides these, if the single server gets down, there is possibility of more attack as Kerberos can’t function its job. These are the major hindrance of Kerberos. This research paper has tried to overcome these security issues, accessing and recovering data by using the RAID level. RAID 5 is the most appropriate technique to recover from failure and in correcting the erroneous data by minimizing redundancy as well as mitigating data loss. Also, RAID 5 can be used in case of distributed data. So, a model has been proposed to configure RAID 5 to ensure better security of cloud storage by overcoming the limitations of Kerberos.

Varieties of environmental sources of noise and distortion can degrade the quality of the speech signal in a communication system. This research work explores the effects of these interfering sounds on speech applications and introduces a technique for reducing their influence and enhancing the acceptability and intelligibility of the speech signal. In this work, a noise reduction system using single microphone method in time domain to improve SNR of noise contaminated speech is proposed. Traditional Spectral Subtraction method has been reviewed very well and the relationship with wiener filter is also illustrated. The Spectral Subtraction method has been generalized and the focus is put on reducing noise from speech in single channel signals. Voice Activity Detector (VAD) is ignored in this proposed system, because a-priori information about the noise is assumed. The research has been conducted using Gaussian White Noise and Color Noise. The experimental result shows a remarkable improvement in SNR for the generalized version and it is noticed that the result is very much satisfactory when white noises are added but the addition of color noise produces a comparatively poor improvement report. The system has been tested with eight different datasets and on an average, 65.27% improvement in SNR (Signal to Noise Ratio) for White Noise using Generalized Spectral Subtraction Method is achieved comparing with Traditional Spectral Subtraction Method. The average improvement in SNR for Color Noise recorded is 53.31%. The Generalized Spectral Subtraction method is shown to improve the speech quality and to improve SNR as well.

Cloud computing is being considered as a revolutionary technique in the field of enterprise hardware and software design and development. As the popularity of cloud computing is increasing day by day and it’s the mostly used technique in the online and automated business system, so the security of the stored data in the cloud and the accessing method of the data through the network must be ensured. Among the various types of server and network authentication protocol Kerberos is the latest and robust network protocol. For authentication, Kerberos is badly in need of a hosted server which has to be remaining running 24*7 and the access point should be a single server (centralized authentication service). Besides these, if the single server gets down, there is possibility of more attack as Kerberos can’t function its job. These are the major hindrance of Kerberos. This research paper has tried to overcome these security issues, accessing and recovering data by using the RAID level. RAID 5 is the most appropriate technique to recover from failure and in correcting the erroneous data by minimizing redundancy as well as mitigating data loss. Also, RAID 5 can be used in case of distributed data. So, a model has been proposed to configure RAID 5 to ensure better security of cloud storage by overcoming the limitations of Kerberos.