Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Steganography & Support vector machine. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

A Deterministic Multi-layered Partitioning Tool for Wire-Length Reduction of Monolithic 3D-IC

Abstract: As and when Moore's law started to falter, threedimensional integrated circuits (3D-ICs) emerged as a natural alternative to tackle the problem. Monolithic 3D-IC is one of the recent technology that was introduced in the 3D-IC technology, where MIVs (Monolithic Inter-tier Vias) are used for connecting modules that spread over multiple layers. In this paper, we focus on designing the multi layer partitioning tool before placement of cells in such a way that cost of wires is minimized along with balanced area of tiers. We have not put any constraint on the number of MIVs as it does not suffer from any disadvantages like TSV (Through Silicon Via). We implement our algorithm on a set of 3D-GSRC benchmark circuits present in the URL "3D GSRC benchmark http://cadlab.cs.ucla.edu/three d/3dic.html".

Study on DC-DC Converter fed Sensor-less Motor Drive

Abstract: Brushless DC motor widely used in different sectors for high efficiency and very low maintenance. The report concerns about the performances of Brushless DC (BLDC) motor with KY and four quadrant converters. Input voltage has been varied for both of them and instantaneously the output voltage is recorded along with the theoretical value. The simulation of the converter circuits have been made using MATLAB/Simulink platform. The parameters of the BLDC motor under argument are stator back electromotive force (emf), electromagnetic torque and motor speed. An attentive determination has been made to represent the motor parameters in a simply comparable manner for both the converters. Lastly, a conclusion has been drawn on the best outcome of the converter among the two to enable the motor to function efficiently and help us get desirable results when the motor is in operation.

Scalable recommendations using decomposition techniques based on Voronoi diagrams

Abstract: Collaborative filtering based recommender systems typically suffer from scalability issues when new users and items join the system at a very rapid rate. We tackle this concerning issue by employing a decomposition based recommendation approach. We partition the users in the recommendation domain with respect to location using a Voronoi Diagram and execute the recommender algorithm individually in each partition (cell). This results in a much reduced recommendation time as we eliminate the need for running the algorithm using the entire user set. We further address the problem of improving the recommendation quality of the users residing in the peripheral region of a Voronoi cell. The primary objective of our approach is to bring down the recommendation time without compromising the accuracies of recommendations much, which is rightly addressed by our proposed method. The outcomes of the experiments performed demonstrate the scalability as well as efficacy of our method by reducing the runtime of the baseline CF algorithm by at least 65% for each of these four publicly available datasets of varying sizes — MovieLens-100K, MovieLens-1M, Book-Crossing and TripAdvisor datasets. The accuracies of recommendations in terms of MAE, RMSE, Precision, Recall and F1 metrics also hold good.

Radiation from simple and perturbed arbitrarily curved microstrip lines

Abstract: An arbitrarily curved microstrip line supporting travelling wave type current distribution is analyzed for its radiated far-fields The curved microstrip line is segmented into multiple linear segments and radiated fields from each of them are calculated through a rectilinear transformation Finally all such contributions are added up to obtain the total radiated fields A sinusoidal curve based line is simulated in CST Microwave Studio and simulated results match well with the calculated values Further, this sinusoidal microstrip line is loaded with two different types of periodic perturbations and changes in radiated far fields in the x-z plane is observed using the arbitrary microstrip line model Significant amount of beam steering is observed

An End-to-End Deep Learning Framework for Super-Resolution Based Inpainting

Abstract: Image inpainting is an extremely challenging and open problem for the computer vision community. Motivated by the recent advancement in deep learning algorithms for computer vision applications, we propose a new end-to-end deep learning based framework for image inpainting. Firstly, the images are down-sampled as it reduces the targeted area of inpainting therefore enabling better filling of the target region. A down-sampled image is inpainted using a trained deep convolutional auto-encoder (CAE). A coupled deep convolutional auto-encoder (CDCA) is also trained for natural image super resolution. The pre-trained weights from both of these networks serve as initial weights to an end-to-end framework during the fine tuning phase. Hence, the network is jointly optimized for both the aforementioned tasks while maintaining the local structure/information. We tested this proposed framework with various existing image inpainting datasets and it outperforms existing natural image blind inpainting algorithms. Our proposed framework also works well to get noise resilient super-resolution after fine-tuning on noise-free super-resolution dataset. It provides more visually plausible and better resultant image in comparison of other conventional and state-of-the-art noise-resilient super-resolution algorithms.