GNSS-Based Attitude Determination Techniques - A Comprehensive Literature Survey

GNSS-based Attitude Determination (AD) of a mobile object using the readings of the Global Navigation Satellite Systems (GNSS) is an active area of research. Numerous attitude determination methods have been developed lately by making use of various sensors. However, the last two decades have witnessed an accelerated growth in research related to GNSS-based navigational equipment as a reliable and competitive device for determining the attitude of any outdoor moving object using data demodulated from GNSS signals. Because of constantly increasing number of GNSS-based AD methods, algorithms, and techniques, introduced in scientific papers worldwide, the problem of choosing an appropriate approach, that is optimal for the given application, operational environment, and limited financial funding becomes quite a challenging task. The work presents an extensive literature survey of the methods mentioned above which are classified in many different categories. The main aim of this survey is to help researchers and developers in the field of GNSS applications to understand pros and cons of the current state of the art methods and their computational efficiency, the scope of use and accuracy of the angular determination.https://doi.org/10.1109/ACCESS.2020.297008

Analysis of Volatility Volume and Open Interest for Nifty Index Futures Using GARCH Analysis and VAR Model

The generalized autoregressive conditional heteroscedastic model (GARCH) is used to estimate volatility for Nifty Index futures on day trades. The purpose is to find out if a contemporaneous or causal relation exists between volatility volume and open interest for Nifty Index futures traded on the National Stock Exchange of India, and the extent and direction of these relationships. A complete absence of bidirectional causality in any particular instance depicts noise trading and empirical analysis according to this study establishes that volume has a stronger impact on volatility compared to open interest. Furthermore, the impulse originating from volatility of volume and open interest is low.https://doi.org/10.3390/ijfs901000

Predicting Emerging Trends on Social Media by Modeling it as Temporal Bipartite Networks

The behavior of peoples' request for a post on online social media is a stochastic process that makes post's ranking highly skewed in nature. We mean peoples interest for a post can grow/decay exponentially or linearly. Considering this nature of the evolutionary peoples' interest, this paper presents a Growth-based Popularity Predictor (GPP) model for predicting and ranking the web-contents. Three different kinds of web-based real datasets namely Movielens, Facebook-wall-post and Digg are used to evaluate the performance of the proposed model. This performance is measured based on four information-retrieval metrics Area Under receiving operating Characteristic (AUC), Novelty, Precision, and Kendal's Tau. The obtained results show that the prediction performance can be further improved if the score is mapped onto a cumulative predicted item's ranking.https://doi.org/10.1109/ACCESS.2020.297613

Partial Observer Decision Process Model for Crane-Robot Action

The most common use of robots is to effectively decrease the human’s effort with desirable output. In the human-robot interaction, it is essential for both parties to predict subsequent actions based on their present actions so as to well complete the cooperative work. A lot of effort has been devoted in order to attain cooperative work between human and robot precisely. In case of decision making , it is observed from the previous studies that short-term or midterm forecasting have long time horizon to adjust and react. To address this problem, we suggested a new vision-based interaction model. The suggested model reduces the error amplification problem by applying the prior inputs through their features, which are repossessed by a deep belief network (DBN) though Boltzmann machine (BM) mechanism. Additionally, we present a mechanism to decide the possible outcome (accept or reject). The said mechanism evaluates the model on several datasets. Hence, the systems would be able to capture the related information using the motion of the objects. And it updates this information for verification, tracking, acquisition, and extractions of images in order to adapt the situation. Furthermore, we have suggested an intelligent purifier filter (IPF) and learning algorithm based on vision theories in order to make the proposed approach stronger. Experiments show the higher performance of the proposed model compared to the state-of-the-art methods.https://doi.org/10.1155/2020/634934

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely

Goal directed design of serial robotic manipulators based on task descriptions

This thesis is being archived as a Digitized Shelf Copy for campus access to current students and staff only. We currently cannot provide this open access without the author's permission. If you are the author of this work and desire to provide it open access or wish access removed, please contact the Wahlstrom Library to discuss permission.The goal of robotics is to automate and delegate real-world tasks to robotic manipulators. Today robots are being applied to wide range of tasks; from the very traditional material handling tasks to the very sophisticated tele-robotic surgery. Even though general-purpose manipulators are commonplace they do not guarantee optimal task performance. Task optimized manipulators are more effective and efficient than general purpose manipulators. There is a great need for task optimized industrial manipulators that can perform a certain set of jobs with the best efficiency, in the shortest time, and with the least operating cost and power requirements. Computing the optimal geometric structure of manipulators is one of the most intricate problems in contemporary robot kinematics. Robotic manipulators are designed and built to perform certain predetermined tasks. It is therefore important to incorporate such task requirements during the design and synthesis of the robotic manipulators. Such task requirements and performance constraints can be specified in terms of the required end-effector positions, orientations and velocities along the task trajectory. There is a close relation between the structure of the manipulator and its kinematic performance. Robotic researchers have over the years tried to develop a framework to reverse engineer optimal manipulator geometries based on task requirements. Every robotic manipulator can only perform certain set of a set of tasks, and some more efficiently than others. Deciding the best manipulator structure for a required job at the design stage is done mainly on the basis of experience and intuition. The rigorous analysis of a few widely used manipulator structures and a collection of a few ad hoc analytical tools can be of some help. However, the need for a comprehensive framework to reverse engineer manipulator structures from task descriptions that can guarantee optimal task performance under a set of operating constraints is still lacking. The ultimate goal of this task-based design approach is to be able to generate both the kinematic and dynamic parameters from task descriptions and operating constraints. In this work, we define, develop and test a methodology that can generate optimal manipulator geometric structures based on the task requirements. Another objective of this work is to guarantee task performance under user defined joint constraints. Using this methodology, task-based optimal manipulator structures can be generated that guarantee task performance under set operating constraints

Online Automation & Control: An Experiment in Distance Engineering Education

Online distance education is the latest trend in the education industry. Even though most of the courses offered today are non-technical, there is a large market for online technical education leading to graduate and undergraduate accredited degrees. This paper studies the current state of the online education industry and proposes ways and means for providing comprehensive laboratory based technical degrees through online distance education. The paper also presents innovative approaches and coursework developed at the University of Bridgeport, to provide laboratory-based accredited programs

Online Automation & Control: An Experiment in Distance Engineering Education

Online distance education is the latest trend in the education industry. Even though most of the courses offered today are non-technical, there is a large market for online technical education leading to graduate and undergraduate accredited degrees. This paper studies the current state of the online education industry and proposes ways and means for providing comprehensive laboratory based technical degrees through online distance education. The paper also presents innovative approaches and coursework developed at the University of Bridgeport, to provide laboratory-based accredited programs