<p>Analysis of brain signals and their properties provides valuable information regarding the underlying neural deficiencies and enables the diagnosis of attention bias related to public speaking anxiety (PSA). Although 25% people around the world suffer from PSA, currently, there exists a lack of standard assessment in diagnosing the severity of attention bias in individuals with PSA. This study aims to distinguish behavioral and neural abnormalities related to attentional bias during PSA by comparing reaction time (RT) and event-related potential (ERP) correlates of high (H) PSA and low (L) PSA individuals. 12 individuals suffering from HPSA and 12 individuals with LPSA participated in the modified emotional Stroop experiment. Electroencephalography (EEG) was recorded with the low cost, 14-channel Emotiv Epoc+. RT showed slower responses, linked to attentional deficits in HPSA individuals. ERP results revealed the P200 emotional Stroop biomarker, found to be linked to attentional bias in HPSA, but not in LPSA individuals. These results revealed significant RT and P200 ERP abnormalities related to attentional bias in HPSA individuals using the low-cost Emotiv Epoc+.</p>
As the wireless sensor networks (WSNs) progress with newer and more advanced technologies, so do the demands for them in a growing number of applications. Precision agricultural environment monitoring is one of the most prominent applications that require feasible wireless support systems, particularly in the protection and condition control of the crops. This paper focuses on the grid nodes arrangement of WSN, considering the wide dissemination of the plantation areas in the agriculture industry. Due to the different types of sensors used and their data size, the study on the impact of the varied packet size on the performance of the small and large network has been carried out using AODV and OLSR routing protocols. No significant differences in terms of performance can be seen as the packet size is varied. However, compared to the small network, more performance issues have occured in the large network, such as more packet loss, higher throughput degradation, higher energy consumption, worse unfairness, and more overhead production. The OEG routing protocol has been proposed to enhance the network performance by reducing the strain due to the saturated traffic. When solely compared to AODV, OEG routing protocol is able to enhance the network performance with at most 27% more packet delivery ratio, 31kbps more throughput, and 0.991J lesser energy consumed in the network.
Oil and gas industry is one of the largest corporations in the world. The process and machinery involved for the petroleum product to be extracted, refined and transported are usually critical and complex. Hence, it is crucial to have a stable and reliable system to control, monitor and manage the security the industrial assets. Since Wireless Sensor Network (WSN) can sense, process and communicate, it is one of the best and popular solution to the problem existed in the oil and gas industry. In this paper, a detailed performance analysis of AODV, DSDV and FIXRT with 32 and 128 packet size is presented by using linear topology in accordance with IEEE 802.11 standards by using ns2.35 simulator. The results showed that FIXRT protocol increases the overall performance of the network by almost 30%.
The arcus cornea is an eye problem that is often encountered among older people, but the situation is risky to teenagers and young people, in which it gave a sign the presence of lipid abnormalities in their blood and the risk of disease problems such as diabetes and heart disease. This paper presents the classification of the arcus cornea, using the extraction of texture features of the gray level co-occurrence matrix, along with several models of the classifiers, namely as scale conjugate gradient, Bayesian regulation, and Levenberg-Marquardt. Percentage fractions for training, testing and validation for classifier are 70%, 15%, and 15% respectively. The comparison of the classifiers used by the past researchers for classification the eye abnormalities, also were analyzed and studied in this work. In this experiment, a total of 125 image eyes were used, consisting of two classes of the eye image, which is normal and abnormal. The best result demonstrated in this proposed framework using Bayesian regulation classifier is, a sensitivity of 96%, and a specificity of 100%. However, this classifier did not achieve perfectly classification or an accuracy of 100%. Nevertheless, it is able and evident that the system is effective by the output of 98.4% accuracy.
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