Browsing by Author "Noor, Md Sadaf"

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Mixed FBB and RBB low leakage technique for high durable CMOS circuit
(© 2014 IEEE Computer Society, 2014) Barua, Parag; Jafar, Imran Bin; Sengupta, Prianka; Noor, Md Sadaf
CMOS logic circuit is extensively used for designing low power Very Large Scale Integration (VLSI). Reducing the dimension of CMOS in a nanometer range, functionality and efficiency can be increased, but as a result we have to compromise with circuit level leakage. As circuit level leakage also known as leakage current is currently one of the major concernments to the VLSI designers. These Leakage currents are generated due to different types of leakage current components such as Weak inversion current, Drain-induced barrier lowering (DIBL), Gate-induced drain leakage and Oxide leakage tunneling. However, there are wide ranges of method that are already available to reduce these leakages, but all of them have their own tradeoffs. In this paper we propose a novel technique by integrating the idea of Forward Back Bias (FBB) and Reverse Back Bias (RBB) which reduces leakage extensively than sleepy stack, stacked sleep, variable body biasing and dual sleep. Furthermore, RBB and FBB are yielded with forced stacked transistors where RBB is accountable for nullifying the leakage and FBB is responsible for offsetting the delay penalty. The proposed method is scrutinized under 22nm to 65nm feature size, and it has come out that these novel schemes are especially very effective for designing the future low-voltage, low-power CMOS VLSI's [1]. Therefore, the main principle of this technique is to trim down leakages, but it has an obvious delay constraint that is considered as a tradeoff in this particular case.
Recommendation framework: Improving automated collaborative filtering by trusted category
(BRAC University, 2015-12) Noor, Md Sadaf; Ahmed, Tarem
The key idea behind user user automated collaborative filtering is that it predicts item's rating based on similar users who share the same taste by rating item similarly. Automated collaborative filtering (acf) is proposed on a hypothesis that users with similar rating will also have similar rating in everything. People often agree on an idea, or on a group of ideas but it is very rare that agree on everything. So in this thesis we suggest that this is one of the main causes behind huge noises while working with a large number of neighbors in acf. In this thesis we are proposing a method where at the time of calculating acf we will only consider a subset of products based on their category that we trust based on users previous ratings. Usually when a developer has to build a recommendation system he has to start writing code from scratch like in the early days of web development, which consumes a lot of time and development energy so we tried to build a framework for our updated acf that provides the building blocks for a recommendation system as APIs. By describing models of their recommender in simple JSON format, within a few commands and using provided API support, programmers can easily get a customizable generic service running. All the input and output communications are done using RESTful APIs so that the system can communicate with any part of the whole system.