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.

AD3S: Advanced Driver Drowsiness Detection System using Machine Learning

Abstract: Drivers drowsiness is one of the prime reasons for road accidents around the globe. Persistent monotonous driving for an extended period of time without rest leads to drowsiness and fatal mishaps. Automatic detection of driver's drowsiness can prevent a large number of road accidents and thus, can save valuable lives. In this work, an advanced system namely AD3S (Advanced Driver Drowsiness Detection System) using Android application has been developed. The system is capable of capturing real-time facial landmarks of the drivers. The facial landmarks are further utilized to compute several parameters namely Eye Aspect Ratio (EAR), Nose Length Ratio (NLR) and Mouth Opening Ratio (MOR) based on adaptive threshold which are capable of detecting driver's drowsiness. The highlighting feature of AD3S is that it is non-intrusive in nature and is cost effective. To test the efficacy of AD3S, machine learning and deep learning techniques have been applied over a data set of 1200 application users. The empirical results demonstrate that the proposed system is capable of detecting driver's drowsiness with an accuracy of approximately 98% with Bagging classifier.

A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen

Abstract: Ultrasound (US) imaging is an indispensible technique for detection of abdominal stones which are a serious health hazard. Segmentation of stones from abdominal ultrasound images presents a unique challenge because these images contain strong speckle noise and attenuated artifacts. In clinical situations where a large number of stones must be identified, traditional methods such as manual identification become tedious and lack reproducibility too. The necessity of obtaining high reproducibility and the need to increase efficiency motivates the development of automated and fast procedures that segment out stones of all sizes and shapes in medical images by applying image segmentation techniques. In this paper we present and compare two fully automatic and unsupervised methods for robust stone detection in B-mode ultrasound images of the abdomen. Our approaches are based on the marker controlled watershed segmentation, along with some pre-processing and post-processing procedures that eliminate the inherent problems associated with medical ultrasound images. The first algorithm (Algorithm I) utilizes the advantage of the Speckle reducing anisotropic diffusion (SRAD) technique, along with unsharp filtering and histo- gram equalization for removal of speckle noise, and the second algorithm (Algorithm II) is based on the log decompression model which too serves as a tool for minimization of speckle. Experimental results obtained from processing a set of 50 ultrasound images ensure the robustness of both the proposed algorithms. Comparative results of both the algorithms based on efficiency and relative error in stone area have been provided.

Intuitionistic Fuzzy Genetic Weighted Averaging Operator and its Application for Multiple Attribute Decision Making in E-Learning

Abstract: Background/Objectives: Adaptation and personalization of E-learning systems require efficient learner modeling. Attributes of learner are evaluated to classify their knowledge without considering the weight difference with respect to their similarity level in E-learning environment for intuitionistic fuzzy data. Methods/Statistical analysis: This paper proposes an Intuitionistic Fuzzy Weighted Averaging (IFWA) operator. The IFWA operator is combined with Genetic Algorithm (GA) to tune the weight of the attributes of learners with respect to their similarity level. The proposed model tests and evaluates the IFWA algorithm on user knowledge modeling data set taken from UC irvine machine learning repository. Findings: The algorithm measures the performance in terms of the best weight values corresponding to the classification. Intuitionistic fuzzy data set is compared based on mean error for different run of generations' with best weight values. The mean square error .002349 proves the consistent performance of the algorithm to allocate weight to the attributes in intuitionistic fuzzy domain. Applications/Improvements: The proposed Intuitionistic Fuzzy Genetic Weighted Averaging Algorithm (IFGWA) can play an efficient role in various decision making problems.

Generic System Verilog Universal Verification Methodology based Reusable Verification Environment for Efficient Verification of Image Signal Processing IPs/SoCs

Abstract: In this paper, we present Generic System Verilog Universal Verification Methodology based Reusable Verification Environment for efficient verification of Image Signal Processing IP’s/SoC’s. With the tight schedules on all projects it is important to have a strong verification methodology which contributes to First Silicon Success. Deploy methodologies which enforce full functional coverage and verification of corner cases through pseudo random test scenarios is required. Also, standardization of verification flow is needed. Previously, inside imaging group of ST, Specman (e)/Verilog based Verification Environment for IP/Subsystem level verification and C/C++/Verilog based Directed Verification Environment for SoC Level Verification was used for Functional Verification. Different Verification Environments were used at IP level and SoC level. Different Verification/Validation Methodologies were used for SoC Verification across multiple sites. Verification teams were also looking for the ways how to catch bugs early in the design cycle? Thus, Generic System Verilog Universal Verification Methodology (UVM) based Reusable Verification Environment is required to avoid the problem of having so many methodologies and provides a standard unified solution which compiles on all tools. The main aim of development of this Generic and automatic verification environment is to develop an efficient and unified verification environment (at IP/Subsystem/SoC Level) which reuses the already developed Verification components and also sequences written at IP/Subsystem level can be reused at SoC Level both with Host BFM and actual Core using Incisive Software Extension (ISX) and Virtual Register Interface (VRI)/Verification Abstraction Layer (VAL) approaches. IP-XACT based tools are used for automatically configuring the environment for various imaging IPs/SoCs. Although this paper focus on Generic System Verilog Universal Verification Methodology based reusable verification environment built for imaging IPs/SoCs. Same concept can be extended for non imaging IPs/SoCs.