SoA Based Carry Lookahead Adder Using Reversible MZI

Authors:- Andiboyina Vijayasri, B. Murali Krishna

Abstract:- Carry look ahead adder (CLA) is the fastest of all adders and achieve speed through parallel carry computations. This method does not require the carry signal to propagate stage by stage. In this project, an efficient reversible implementation of Carry-Look ahead Adder (CLA) in all-optical domain is presented. Now-adays, semiconductor optical amplifier (SOA)- based Mach–Zehnder interferometer (MZI) plays a vital role in the field of ultra-fast all-opsoa-based-carry-lookahead-adder-using-reversible-mzitical signal processing. We have used all optical based Mach-Zehnder Interferometer (MZI) switches to design the CLA circuit implementing reversible functionality. The design of reversible circuit with optical technology can be implemented using Semiconductor Optical Amplifier (SOA) based Mach-Zehnder Interferometer (MZI) switch, which has significant advantages of high speed, low power, fast switching time and ease of fabrication. A conventional carry select adder is still power consuming due to the dual ripple carry adder structures having cin as ‘0’ and ‘1’. An efficient method which replaces the ZCLA instead of RCA with cin=’0’in the conventional CSLA is efficient and this CLA with the Mach zehnder interferometer technique can be more helpful and advantageous for the digital optical fibre communications.

Authentication of Ranking Deceit for Mobile Apps

Authors:- Yalavarthipati Shaik Reshma, B Lakshmikanth

Abstract:- The Mobile App is a very popular and well known concept due to the rapid advancement in the mobile technology. Due to the large number of mobile Apps, ranking fraud is the key challenge in front of the mobile App market. Ranking fraud refers to fraudulent or vulnerable activities which have a purpose of bumping up the Apps in the popularity list. While the importance and necessity of preventing ranking fraud has been widely recognized. In the existing system the leading event and leading session of an app is identified from the collected historical records. Then three different types of evidences are collected from the user feedbacks namely ranking based evidence, rating based evidence and review based evidence. These three evidences are aggregated by using evidence aggregation method. In the proposed system additionally, we are proposing two enhancements. Firstly, we are using Approval of scores by the admin to identify the exact reviews and rating scores. Secondly, the fake feedbacks by a same person for pushing up that app on the leader board are restricted. Two different constraints are considered for accepting the feedback given to an application. The first constraint is that an app can be rated only once from a user login and the second is implemented with the aid of IP address that limits the number of user login logged per day. Finally, the proposed system will be evaluated with real-world App data which is to be collected from the App Store for a long time period.
Active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of global anomaly of app rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling apps’ ranking, rating and review behaviors through statistical hypotheses tests.
In Rating Based Evidences, specifically, after an App has been published, it can be rated by any user who downloaded it. Indeed, user rating is one of the most important features of App advertisement. An App which has higher rating may attract more users to download and can also be ranked higher in the leader board. Thus, rating manipulation is also an important perspective of ranking fraud. In Review Based Evidences, besides ratings, most of the App stores also allow users to write some textual comments as App reviews. Especially, this paper proposes a simple and effective algorithm to recognize the leading sessions of each mobile App based on its historical ranking records. This is one of the fraud evidence. Also, rating and review history, which gives some anomaly patterns from apps historical rating and reviews records.
The rest of the paper is organized as follows: Section II, presents the literature survey over the related work. In section III, proposed system is presented in section IV, implementation for each modules. Finally, the section V concludes the review paper.