The ARIEL Instrument Control Unit design for the M4 Mission Selection Review of the ESA's Cosmic Vision Program

The Atmospheric Remote-sensing Infrared Exoplanet Large-survey mission (ARIEL) is one of the three present candidates for the ESA M4 (the fourth medium mission) launch opportunity. The proposed Payload will perform a large unbiased spectroscopic survey from space concerning the nature of exoplanets atmospheres and their interiors to determine the key factors affecting the formation and evolution of planetary systems. ARIEL will observe a large number (>500) of warm and hot transiting gas giants, Neptunes and super-Earths around a wide range of host star types, targeting planets hotter than 600 K to take advantage of their well-mixed atmospheres. It will exploit primary and secondary transits spectroscopy in the 1.2-8 um spectral range and broad-band photometry in the optical and Near IR (NIR). The main instrument of the ARIEL Payload is the IR Spectrometer (AIRS) providing low-resolution spectroscopy in two IR channels: Channel 0 (CH0) for the 1.95-3.90 um band and Channel 1 (CH1) for the 3.90-7.80 um range. It is located at the intermediate focal plane of the telescope and common optical system and it hosts two IR sensors and two cold front-end electronics (CFEE) for detectors readout, a well defined process calibrated for the selected target brightness and driven by the Payload's Instrument Control Unit (ICU).Comment: Experimental Astronomy, Special Issue on ARIEL, (2017

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Comparative Analysis of Data Security and Cloud Storage Models Using NSL KDD Dataset

Cloud computing is becoming increasingly important in many enterprises, and researchers are focusing on safeguarding cloud computing. Due to the extensive variety of service options it offers, A significant amount of interest from the scientific community has been focused on cloud computing. The two biggest problems with cloud computing are security and privacy. The key challenge is maintaining privacy, which expands rapidly with the number of users. A perfect security system must efficiently ensure each security aspect. This study provides a literature review illustrating the security in the cloud with respect to privacy, integrity, confidentiality and availability, and it also provides a comparison table illustrating the differences between various security and storage models with respect to the approaches and components of the models offered. This study also compares Naïve Bayes and SVM on the accuracy, recall and precision metrics using the NSL KDD dataset

CROWD-2-CLOUD – Remote Sensing Land Cover Verification With Crowd-Sourcing Data

Nowadays, advanced remote sensing technologies provide huge amount of Earth Observation (EO) data timely. Growing quickly in terms of size and structure, EO data require a new way of handling and processing as it is considered big data. Cloud-computing platform proved to be a reliable and scalable platform that suits various user demands in remote sensing data processing. To verify the ambiguity of information derived solely from remote sensing, ground data is vital. The only way to keep pace with big remote sensing data is to exploit the crowdsourced data, which has been recently proposed elsewhere. In this study, we developed a prototype of an integrated location based service on top of cloud computing platform to detect land cover features and engage the crowd of volunteers during training and verification process. Relying on open-source tools, the proposed system provides location-based data collection and satellite image classification. The prototype was tested over the rapid on-going landscape surrounding the University of Nottingham, Malaysia campus. More advanced functions will be developed and a full system will be deployed and tested in further study

SENTIMENT ANALYSIS ON E-SPORTS FOR EDUCATION CURRICULUM USING NAIVE BAYES AND SUPPORT VECTOR MACHINE

The development of e-sports education is not just playing games, but about start making, development, marketing, research and other forms education aimed at training skills and providing knowledge in fostering character. The opinions expressed by the public can take form support, criticism and input. Very large volume of comments need to be analyzed accurately in order separate positive and negative sentiments. This research was conducted to measure opinions or separate positive and negative sentiments towards e-sports education, so that valuable information can be sought from social media. Data used in this study was obtained by crawling on social media Twitter. This study uses a classification algorithm, Naïve Bayes and Support Vector Machine. Comparison two algorithms produces predictions obtained that the Naïve Bayes algorithm with SMOTE gets accuracy value 70.32%, and AUC value 0.954. While Support Vector Machine with SMOTE gets accuracy value 66.92% and AUC value 0.832. From these results can be concluded that Naïve Bayes algorithm has a higher accuracy compared to Support Vector Machine algorithm, it can be seen that the accuracy difference between naïve Bayes and the vector machine support is 3.4%. Naïve Bayes algorithm can thus better predict the achievement of e-sports for students' learning curriculum

Business intelligence framework using ant colony optimization for feature selection in higher education institution

Recently, business intelligence (BI) has become an important tool for effective decision-making. BI is a mathematical framework to gain information and knowledge through the process of extracting, transforming, managing, and analyzing data. The demand for accurate knowledge in higher education sector needs a correct technique to extract the exact information for decision-making. However, current BI frameworks and systems lack the ability to transform data into information, and these caused users not to able to fully utilize the BI outcome. This research developed a BI framework for the higher education that is able to explore, analyse and visualize the relevant data into information for use by the top management. This framework identifies the best set of attributes and evaluates the performance of the model with the help of 27 input features. In this case study, the framework used Ant Colony Optimization (ACO) technique mainly to identify the best set of attributes, and the performance was validated using Support Vector Machine (SVM). The framework consists of four layers which are data source, data integration, analytic, and access. Each layer contributes to decision making in terms of processing data, selection of significant features and data visualization. In this study, 46,658 input data were processed for identification of Graduate on Time (GOT) decision in the context of higher education referred as Masters and Doctor of Philosophy (PhD) postgraduates who completed their study within a specified period. The performance evaluation of the data achieved accuracies of 86.44% for PhD and 96.2% for Master’s. Based on the findings, the results showed that the BI dashboard as an output from the framework is capable of providing a good decision-making tool for education management