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: Cluster analysis & Wireless sensor network. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

A Distributed Framework for Energy Trading Between UAVs and Charging Stations for Critical Applications

Abstract: Use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in various domains such as disaster management, delivery of goods, surveillance, military, etc. Significant issues in the expansion of UAV-based applications are the security of (IoT to UAV) communication, and the limited flight time of the UAVs and IoT devices considering the limited battery power. Standalone UAVs are not capable of accomplishing several tasks, and therefore swarm of UAVs is being explored. Security issues in the swarm of UAVs do not allow the applications to leverage the full benefits that one can offer. Several recent studies have proposed the use of a distributed network of UAVs to upgrade the level of security in the swarm of UAVs. In this paper, a framework for secure and reliable energy trading among UAVs and charging stations is presented. Advanced blockchain, based on the tangle data structure is used to create a distributed network of UAVs and charging stations. The proposed model allows the UAVs to buy energy from the charging station in exchange for tokens. If the UAV does not have sufficient tokens to buy the energy, then the model allows the UAV to borrow tokens from the charging station. The borrowed tokens can be repaid back to the charging station with interest or late fees. A game-theoretic model is used for deciding the buying strategy of energy for UAVs. Numerical analysis shows that the proposed model helps in providing increased utility for the swarm of UAVs and charging stations in a secure and cost-optimal way as compared to the conventional schemes. The results can eventually be applied to IoT devices that constantly need energy to perform under ideal conditions.

Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio

Abstract: Every year many people lose their lives due to fatal road accidents around the world and drowsy driving is one of the primary causes of road accidents and death. Fatigue and micro sleep at the driving controls are often the root cause of serious accidents. However, initial signs of fatigue can be detected before a critical situation arises and therefore, detection of driver’s fatigue and its indication is ongoing research topic. Most of the traditional methods to detect drowsiness are based on behavioural aspects while some are intrusive and may distract drivers, while some require expensive sensors. Therefore, in this paper, a light-weight, real time driver’s drowsiness detection system is developed and implemented on Android application. The system records the videos and detects driver’s face in every frame by employing image processing techniques. The system is capable of detecting facial landmarks, computes Eye Aspect Ratio (EAR) and Eye Closure Ratio (ECR) to detect driver’s drowsiness based on adaptive thresholding. Machine learning algorithms have been employed to test the efficacy of the proposed approach. Empirical results demonstrate that the proposed model is able to achieve accuracy of 84% using random forest classifier.

Statistical mixtures of states can be more quantum than their superpositions: Comparison of nonclassicality measures for single-qubit states

Abstract: A bosonic state is commonly considered nonclassical (or quantum) if its Glauber-Sudarshan $P$ function is not a classical probability density, which implies that only coherent states and their statistical mixtures are classical. We quantify the nonclassicality of a single qubit, defined by the vacuum and single-photon states, by applying the following four well-known measures of nonclassicality: (1) the nonclassical depth, $\ensuremath{\tau}$, related to the minimal amount of Gaussian noise which changes a nonpositive $P$ function into a positive one; (2) the nonclassical distance $D$, defined as the Bures distance of a given state to the closest classical state, which is the vacuum for the single-qubit Hilbert space; together with (3) the negativity potential (NP), and (4) concurrence potential, which are the nonclassicality measures corresponding to the entanglement measures (i.e., the negativity and concurrence, respectively) for the state generated by mixing a single-qubit state with the vacuum on a balanced beam splitter. We show that complete statistical mixtures of the vacuum and single-photon states are the most nonclassical single-qubit states regarding the distance $D$ for a fixed value of both the depth $\ensuremath{\tau}$ and NP in the whole range $[0,1]$ of their values, as well as the NP for a given value of $\ensuremath{\tau}$ such that $\ensuremath{\tau}g0.315\phantom{\rule{0.16em}{0ex}}4$. Conversely, pure states are the most nonclassical single-qubit states with respect to $\ensuremath{\tau}$ for a given $D$, NP versus $D$, and $\ensuremath{\tau}$ versus NP. We also show the ``relativity'' of these nonclassicality measures by comparing pairs of single-qubit states: if a state is less nonclassical than another state according to some measure then it might be more nonclassical according to another measure. Moreover, we find that the concurrence potential is equal to the nonclassical distance for single-qubit states. This implies an operational interpretation of the nonclassical distance as the potential for the entanglement of formation.

Face recognition using principle component analysis, eigenface and neural network

Abstract: Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages - Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network. The algorithm has been tested on 400 images (40 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. The proposed methods were tested on Olivetti and Oracle Research Laboratory (ORL) face database. Test results gave a recognition rate of 97.018%.