Terra and Aqua MODIS TEB Inter-Comparison Using Himawari-8/AHI as Reference

Intercomparison between the two MODIS instruments is very useful for both the instrument calibration and its uncertainty assessment. Terra and Aqua MODIS have almost identical relative spectral response, spatial resolution, and dynamic range for each band, so the site-dependent effect from spectral mismatch for their comparison is negligible. Major challenges in cross-sensor comparison of instruments on different satellites include differences in observation time and view angle over selected pseudoinvariant sites. The simultaneous nadir overpasses (SNO) between the two satellites are mostly applied for comparison and the scene under SNO varies. However, there is a dearth of SNO between the Terra and Aqua. This work focuses on an intercomparison method for MODIS thermal emissive bands using Himawari-8 Advanced Himawari Imager (AHI) as a reference. Eleven thermal emissive bands on MODIS are at least to some degree spectrally matched to the AHI bands. The sites selected for the comparison are an ocean area around the Himawari-8 suborbital point and the Strzelecki Desert located south of the Himawari-8 suborbital point. The time difference between the measurements from AHI and MODIS is <5 min. The comparison is performed using 2017 collection 6.1 L1B data for MODIS. The MODISAHI difference is corrected to remove the view angle dependence. The TerraAqua MODIS difference for the selected TEB is up to 0.6 K with the exception of band 30. Band 30 has the largest difference, which is site dependent, most likely due to a crosstalk effect. Over the ocean, the band 30 difference between the two MODIS instruments is around 1.75 K, while over the desert; the difference is around 0.68 K. The MODIS precision is also compared from the Gaussian regression of the double difference. Terra bands 27 to 30 have significant extra noise due to crosstalk effects on these bands. These TerraAqua comparison results are used for MODIS calibration assessments and are beneficial for future calibration algorithm improvement. The impact of daytime measurements and the scene dependence are also discussed

A Mixed Methods Approach to Understanding Participant Perspectives on Return of Individual Research Results

Provision of individual research results to participants is a critical component of the research process. While there is general interest amongst researchers in returning individual research results, a lack of understanding of the personal value of results for participants has hindered the return of individual results. This is especially true for non-genomic research results such as surveys, laboratory test results, or imaging results. This study examined the participant perspectives on the return of individual research results in a diverse cohort of 1587 mothers currently enrolled in the Environmental Child Health Outcomes (ECHO) program. A mixed-methods approach was used to delineate the influence of result type and standardization status (availability of normative data) on the perceived value of individual research results. Racial differences between American Indian and White participants with respect to perceived value of individual research results were examined. Additionally, the study explored the process by which participants make decisions regarding value of individual research results. Findings from this study indicate that irrespective of result type, participants attributed higher perceived value to individual research results that were framed within a normative context than those that were not framed within a normative context. No significant differences were found between American Indian and White participants with respect to perceived value of individual research results. Qualitative interviews showed that participants’ process of attributing value to individual research results is influenced by others’ experiences including advice from the researcher

Review of Coupled Bunch Instabilities in the LHC

In order to reach the required luminosity, the LHC will have a large number of high intensity bunches. Coupled bunch instabilities can therefore be excited by the higher order modes (HOMs)of the RF cavities, by parasitic cavities and by the transverse resistive wall effect. This report summarises the growth times of the coupled bunch instabilities taking into account the HOMs (damped or undamped)relevant for the 200 MHz normal conducting cavities, the 400 MHz superconducting cavities, as well as other parasitic cavities. It is shown that, with the damped HOMs of the RF cavities, the coupled bunch instabilities remain within control for the LHC operation.As far as the transverse resistive wall effect at injection disconcerned,it is demonstrated that the corresponding growth times can be safely compensated by the proposed transverse feedback system [1]

Effect of migration, carrying capacity, and fecundity on the formation of clinal patterns during range expansions.

Range expansions, empirically and in simulations, lead to clinal patterns of genetic diversity. Clines are often used as spatial markers of past migrations. This study investigated the effects of migration, growth, and carrying capacities on clinal patterns during range expansions, using forward-time simulations in Nemo. Initial results show, in the absence of prior population structure, range expansions result in a loss of diversity strongly affected by migration, growth, and carrying capacity. This loss of diversity did not persist to the final generation, corresponding to 10,000 years, indicating clinal patterns are less durable than previously assumed—challenging the utility of clinal patterns as specific markers of past migrations. Further simulations are necessary to evaluate the effects of large demographic collapses, negative selection, and non equilibrium migration upon clines. While the case study for these experiments is the peopling of Europe, these results are broadly applicable to other human colonization events

A Comparative Analysis of EEG-based Stress Detection Utilizing Machine Learning and Deep Learning Classifiers with a Critical Literature Review

Background: Mental stress is considered to be a major contributor to different psychological and physical diseases. Different socio-economic issues, competition in the workplace and amongst the students, and a high level of expectations are the major causes of stress. This in turn transforms into several diseases and may extend to dangerous stages if not treated properly and timely, causing the situations such as depression, heart attack, and suicide. This stress is considered to be a very serious health abnormality. Stress is to be recognized and managed before it ruins the health of a person. This has motivated the researchers to explore the techniques for stress detection. Advanced machine learning and deep learning techniques are to be investigated for stress detection.  Methodology: A survey of different techniques used for stress detection is done here. Different stages of detection including pre-processing, feature extraction, and classification are explored and critically reviewed. Electroencephalogram (EEG) is the main parameter considered in this study for stress detection. After reviewing the state-of-the-art methods for stress detection, a typical methodology is implemented, where feature extraction is done by using principal component analysis (PCA), ICA, and discrete cosine transform. After the feature extraction, some state-of-art machine learning classifiers are employed for classification including support vector machine (SVM), K-nearest neighbor (KNN), NB, and CT. In addition to these classifiers, a typical deep-learning classifier is also utilized for detection purposes. The dataset used for the study is the Database for Emotion Analysis using Physiological Signals (DEAP) dataset. Results: Different performance measures are considered including precision, recall, F1-score, and accuracy. PCA with KNN, CT, SVM and NB have given accuracies of 65.7534%, 58.9041%, 61.6438%, and 57.5342% respectively. With ICA as feature extractor accuracies obtained are 58.9041%, 61.64384%, 57.5342%, and 54.79452% for the classifiers KNN, CT, SVM, and NB respectively. DCT is also considered a feature extractor with classical machine learning algorithms giving the accuracies of 56.16438%, 50.6849%, 54.7945%, and 45.2055% for the classifiers KNN, CT, SVM, and NB respectively. A conventional DCNN classification is performed given an accuracy of 76% and precision, recall, and F1-score of 0.66, 0.77, and 0.64 respectively. Conclusion: For EEG-based stress detection, different state-of-the-art machine learning and deep learning methods are used along with different feature extractors such as PCA, ICA, and DCT. Results show that the deep learning classifier gives an overall accuracy of 76%, which is a significant improvement over classical machine learning techniques with the accuracies as PCA+ KNN (65.75%), DCT+KNN (56.16%), and ICA+CT (61.64%)

New Approach for Temporal Stability Evaluation of Pseudo-Invariant Calibration Sites (PICS)

Pseudo-Invariant Calibration Sites (PICS) are one of the most popular methods for in-flight vicarious radiometric calibration of Earth remote sensing satellites. The fundamental question of PICS temporal stability has not been adequately addressed. However, the main purpose of this work is to evaluate the temporal stability of a few PICS using a new approach. The analysis was performed over six PICS (Libya 1, Libya 4, Niger 1, Niger 2, Egypt 1 and Sudan 1). The concept of a Virtual Constellation was developed to provide greater temporal coverage and also to overcome the dependence limitation of any specific characteristic derived from one particular sensor. TOA reflectance data from four sensors consistently demonstrating stable calibration to within 5%the Landsat 7 ETM+ (Enhanced Thematic Mapper Plus), Landsat 8 OLI (Operational Land Imager), Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and Sentinel-2A MSI (Multispectral Instrument)were merged into a seamless dataset. Instead of using the traditional method of trend analysis (Students T test), a nonparametric Seasonal Mann-Kendall test was used for determining the PICS stability. The analysis results indicate that Libya 4 and Egypt 1 do not exhibit any monotonic trend in six reflective solar bands common to all of the studied sensors, indicating temporal stability. A decreasing monotonic trend was statistically detected in all bands, except SWIR 2, for Sudan 1 and the Green and Red bands for Niger 1. An increasing trend was detected in the Blue band for Niger 2 and the NIR band for Libya 1. These results do not suggest abandoning PICS as a viable calibration source. Rather, they indicate that PICS temporal stability cannot be assumed and should be regularly monitored as part of the sensor calibration process

Coupled Bunch Instabilities in the LHC

In the LHC, the coupled bunch instabilities will be mainly driven by the RF cavities and the resistive wall effect. The growth times of these instabilities have been estimated taking into consideration the undamped and damped higher order modes of these cavities. These estimates show that the rise times of the longitudinal coupled bunch instabilities are under control. The proposed transverse feed-back system allows the same conclusion to be drawn for the transverse resistive wall instability

Cognizance of Vehicle Position and Moving using UHF RFID Tags

The cognizance means to detect the moving position of a robot at the particular point. In this method, the detection is to be done with the help of radio-frequency identification (RFID) tags. RFID tags are used in this method are of ultrahigh frequency (UHF). The indoor environmental area where different goods are distributed this method would be useful there. The RFID reader with identical configuration has been attached to a robot which is used to identify the location with the help of RFID tags. The signal received from RFID reader is used to acknowledge the accurate location and to give the direction to robot to move further at end point. This method proves the effectiveness in accurately estimating the vehicle position and giving the direction up to the last point. DOI: 10.17762/ijritcc2321-8169.15070