Vaughn College of Aeronautics and Technology

About: Vaughn College of Aeronautics and Technology is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Gravitational microlensing & Planetary system. The organization has 727 authors who have published 708 publications receiving 14082 citations. The organization is also known as: College of Aeronautics.

Efficient Frequency Domain Feature Extraction Model using EPS and LDA for Human Activity Recognition

Abstract: Activity identification based on machine learning for human computing aims to understand or capture the state of human behavior, its environment, and integrate user by exploiting distinct types of sensors to give adjustment to the exogenous computing system. The ascent of universal computing systems requires our environment a solid requirement for novel methodologies of Human Computer Interaction (HCI). The recognition of human activities, commonly known as HAR can play a vital task in this regard. HAR has an appealing use in the health-care system and monitoring of Daily Living Activities (DLA) of elderly people by offering the input for the development of more interactive and cognitive environments. This paper is presenting a model for the recognition of Human Activities. In this proposed model, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal, and the Linear Discriminant Analysis (LDA) is used as a dimensionality reduction procedure to extract the discriminant features for human daily activity recognition. After completing EPS feature extraction techniques, LDA is performed on those extracted spectra for extracting features using the dimension reduction technique. Finally, the discriminant vocabulary vector is trained by the Multiclass Support Vector Machine (MCSVM) to classify human activities. For validating the proposed scheme, UCI-HAR datasets have been implemented which demonstrates higher recognition accuracy which has been acknowledged.

Multi-Modality Learning for Non-Rigid 3D Shape Retrieval via Structured Sparsity Regularizations

Abstract: Big challenges are usually occurring in non-rigid 3D shape retrieval, for the shapes undergoing arbitrarily non-affine transformations. In this work, a novel design of feature learning approach is proposed for non-rigid 3D shape retrieval, dubbed Structured Sparsity Regularized Multi-Modality Method (SSR-MM). The shape signatures which capture the deformation-invariant characteristics are averaged and stacked for a multi-modality machine learning approach, and a transform matrix based on the structure sparsity regularization is utilized to map those signatures obtaining the discriminative features for retrieval. The proposed framework is evaluated on the publicly available non-rigid 3D human benchmarks, and the experimental results show the efficacy of our contributions and the advantages of our method over existing ones.

Experimental Study on Dynamic Splitting Tensile Failure Mode of Carbon Nanofibers Reinforced Concrete

Abstract: Carbon nanofibers (CNFs) were used as admixtures to modify traditional concrete, and carbon nanofibers reinforced concrete (CNFC) with fiber volume fraction of 0%, 0.1%, 0.2%, 0.3% and 0.5% were prepared. The dynamic splitting tensile tests of concrete with different fiber volume contents under five loading rates were carried out by using the Φ 100 mm split Hopkinson pressure bar (SHPB) test device. Based on the observation and analysis of the failure modes of the specimens, combined with the energy change rate of incident wave, the failure characteristics of CNFC under dynamic splitting tensile load are expounded. The results show that: the addition of CNFs has a certain inhibition effect on the dynamic splitting tensile failure of concrete; the failure modes of the specimens are the central failure along the loading direction; with the increase of the energy change rate of incident wave, the damage degree of the specimen is gradually aggravated.

Nano Satellite Propulsion using FEEP Thrusters

Abstract: A Field Emission Electric Propulsion (FEEP) system for nano-satellite propulsion is discussed. The feasibility of the technology is considered, taking into account both the nano-satellite and the FEEP thruster. In order to meet short-term nano-satellite applications, the emphasis is to understand performance capabilities of the total system in a LEO mission. This is achieved by considering the available technology, development and cost of launch for nano-satellite(s) as well as FEEP propulsion system requirements in order to meet those demands. FEEP propellant (Cesium and Rubidium) contamination is identified as one of the major problems with this propulsion system.