Wireless ad hoc network provides a short range communication medium for mobile devices. In such type of network the components satisfying the infrastructural needs are not presents and each functionalities needs to be performed by inbuilt elements of the nodes which let them work as a router. Routing is one of the key features performed by specifically designed light weighted protocols in these ad-hoc networks. It suffers from various issues includes route discovery, bandwidth management, congestion, location detection, energy effective operations, link handling etc. All of the above and many other functionality is mainly depends upon the position of the mobile nodes. Over the past few years location based networking technologies is changed very abruptly along with a horizontal and vertical growth in number of applications and its users. This dynamic change in topologies makes the task more difficult to resolve it accurately and with limited number or parameters. This paper proposes a novel MPLI (Modified Parametric Location Identification) approach. Apart from only using the x and y coordinates, the suggested work added some more values which includes angle of arrival, time, distance and circular region quadrants for accurate detection. It also provides timely updates of positions so as to make the routing more robust and position aware so as to avoid data losses and connection termination due to mobility.
Pervasive computing means "existing everywhere”. It is the growing trend towards embedding microprocessors in everyday objects so they can communicate information. Due to this, all the embedded and mobile computing devices are becoming more and more pervasive and dynamically adaptive. In this paper we have described how a pervasive computing model helps to achieve dynamic adaptation with the environment and ubiquitously handles the overall environment. This paper sketches a hypothetical pervasive computing scenario, and uses them to identify key capabilities missing from today’s systems. And for so we have demonstrated a pervasive model which can handle various traffic issues such as deadlock prevention and various random cases like car to car collision, sending signals to the vehicles approaching towards the point of collision and to prevent further deadlock problems. In this paper, we have shown previous problems in the era of computing and its solution achieved by this computing technology. The first section of this paper gives introduction to pervasive computing then second section describes the approach overview of this computing technology, third section deals with its layered architecture and fourth concentrates on case study analysis with explanation of hypothetical scenarios fifth, sixth and seventh deals with the proposed work and finally the last section gives the conclusion and references.
This paper establishes the need for a small Domain Specific Language to support rapid testing and diagnosis of cryptographic algorithm. Such a language will require built-in support for modulo arithmetic, which is used for creating mathematical locks in modern crypto systems. The paper also provides a framework/prototype for such a language. It can further be used as a building block for broader set of language specific features. General Terms Cryptographic Algorithms, Domain Specific Language, Compiler, Parser, Lexer.
In this paper, it is described how an Assamese sentence is translated to English using statistical machine translation. Statistical Machine Translation is the paradigm where translations from source to target language are based on statistical models. Moses is used as a platform for Statistical Machine Translation. GIZA++ is also used for wordalignment and IRSTLM for language model training. A Transliteration model is also integrated into the system to deal with out of vocabulary (OOV) words. General Terms Machine Translation, Natural Language Processing.
Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death of female worldwide. Mammogram is one of the most excellent technologies currently being used for diagnosing breast cancer. Computer aided diagnosis helps the radiologists to detect abnormalities earlier than traditional procedures. In this paper, we suggested to use some of features selected to distinguish the benign and malignant breast cancer. Tumor segmented and denoising prior to classification. The accuracy of proposed system was 100%. General Terms Image processing, Pattern recognition, Medical image processing. Keywords Breast Cancer, Mammography, Denoising, Diagnosis, Image Features. 1. INTRODUCTION The uncontrolled division of one cell may cause to emerge mass called tumor. Tumor can be benign or malignant. Benign tumor cannot spread or invade to other parts of the body while malignant tumor grows rapidly and invades its surrounding tissues through causing their damage. Breast cancer is a malignant tissue beginning to grow in the breast. The abnormalities like existence of a breast mass change in shape and dimension of breast, differences in the color of breast skin, breast aches, etc. are the symptoms of breast cancer [1]. Breast cancer is a disease of humans and other mammals; while the overwhelming majority of cases in humans are women, men can also develop breast cancer. Mammography is the most used screening tool for abnormality detection, because it allows an easy way to identify the cancer. The goal of mammography is the early detection of breast cancer, typically through detection of characteristic masses and/or micro-calcifications. Radiologists interpret the mammograms and attempt to identify areas of potential abnormalities. Therefore, the expert radiologist’s is the corner stones of diagnosis the breast cancer; the screening process depends on the radiologist’s ability to detect areas of subtle irregular abnormalities. It is estimated that between 10-30% of women diagnosed with breast cancer have false-negative mammograms [2]. Most of the false-negative cases can be attributed to the radiologist's failure to detect a cancer which could be due to misinterpretation, or simply that the radiologist overlooked the area. It has been demonstrated that an independent second reading can significantly improve the detection rate and decrease the number of false positive cases. Computerized aided can help the physicians, and radiologist’s to detecting and diagnosing the breast cancer; it is a tools to processing and analyzing images as secondary reading. The first step for CAD is the ability to identify the abnormal masses in the breast, while the second step is to diagnosis the masses detected in the first step. Before these two steps implementing a really important preprocessing step has to take place which is the detection or segmentation of the breast region from the background. The analysis of mammogram image can assist the radiologist for early detection of tumor and diagnosis the breast cancer effectively [3]. Computer aided can classify into two main groups: computer-aided detection (CADe) and computer-aided diagnosis (CADx). The first group CADe works with medical images to detect and localize the lesions in the image. While CADx works directly on the lesions to classify them to benign and malignant, it is diagnosis process [4].