Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

3D Multilayer Mesh NoC Communication and FPGA Synthesis

Abstract: Network on chip (NoC) is the latest approach in which multiprocessors are integrated in a single chip and FPGA implementation makes it scalable and reconfigurable. It is the feasible solution for pipelined architecture and parallel processing in multiprocessor system on chip. The research article presents the NoC architecture for flexible and scalable design under 3D mesh topological structure. The design is considered for 8 layers as multilayered architecture. In one layer 64 nodes can communicate with each other. The design is developed with the help of VHDL programming in Xilinx ISE 14.2 software and functionally simulated in Modelsim 10.0 student edition software. The performance of the design is analyzed with hardware parameters and timing utilization parameters on Virtex 5 FPGA. The inter and intra communication among the nodes is verified on the same FPGA. The design is verified on Virtex-5 FPGA with 8, 16, 32, 64 and 128 bit data transfer among nodes when the NoC is fully connected and utilized. The paper also presents the comparative study of the 3D, 8 layer mesh NoC for different cluster size (2 × 2 × 2), (3 × 3 × 3) and (4 × 4 × 4), based on the FPGA synthesis parameters. The scalable architecture is applicable for the nodes communication in a wireless sensor network in which multiple nodes are communicating in defined field and configured in specific topology such as Zigbee standard (IEEE 802.15.4) follow mesh, one of the topology.

Fuzzy logic approach for adaptive test sheet generation in e-learning

Abstract: Good quality test refers to the ability of questions to test the student's cognitive abilities in the most efficient manner. The task of designing and developing a question paper for an adaptive e-learning system depends on organization of questions, concept, activity and learner level. In order to perform a better adaptation or Personalization we propose an e-test classify based on Bloom's classification, also known as taxonomy of learning objective for different type of learner model like rote learner, analytical learner, example base learner, analogy and deductive learner and commonsense based learner. In this paper we use fuzzy approach to evaluate student preferable test in e-learning environment.

A new scheme for watermark extraction using combined noise-induced resonance and support vector machine with PCA based feature reduction

Abstract: This manuscript presents a new scheme for binary watermark extraction using the combined application of noise-induced resonance (NIR) and support vector machine (SVM). The principal component analysis (PCA) is incorporated to minimize the dimension of the feature set obtained from the attacked watermarked image. The scheme utilizes lifting wavelet transform to decompose the original image into three levels, and blocks of low frequency sub-band coefficients are used for embedding purpose. Reference and signature information is embedded by quantizing the maximum and minimum coefficients of the corresponding block. Whereas, to extract the watermark, NIR-based tuning operation is performed. The transformed coefficients of the attacked watermarked image are tuned using iterative procedure of NIR in such a way that the transformed coefficients change their state from low signal-to-noise ratio (SNR) to maximum SNR or enhanced state. Finally, the tuned coefficients are fed into the machine i.e. SVM to classify as binary classes (0 or 1) which result in the corresponding watermark extraction. Experimental results of the proposed algorithm demonstrate noteworthy robustness against various signal processing attacks and remarkable improvements comparing with some of the recent techniques. Also, the scheme fulfills the requirements of image integrity in case of new strategic attack (i.e. print attack).

A new localization using single mobile anchor and mesh-based path planning models

Abstract: Localization is an important issue in field of Wireless Sensor Networks. Range-free approach is most promising solution used for networks due to its low cost and less power consumption. The main limitation of range-free approach is low accuracy as it is affected by many factors such as node density, coverage and topology diversity. This work proposes a solution that achieves higher accuracy and gives higher coverage for all kinds of network scenarios, even for sparse one. The approach is mainly developed to improve traditional range-free (DV-Hop) method by using a single mobile anchor and mesh based path planning models. The single mobile anchor identifies different reference points in uniform manner to locate nodes in the network region. The performance of proposed methods is evaluated via simulations to demonstrate its effectiveness and simulation results confirm adequacy of the proposed solutions with higher accuracy and higher coverage as compared to traditional one.