5153 research outputs found
Sort by
UnFOLD: Collaborative Student Learning in a Creative Agency Environment
No full textPeter van Geldern's poster on the UnFOLD company's internship program
Bias Toward Chiropractic: Effects of Frame Salience in Globalized Media on Google PageRank
No full textMohammed Al-Azdee and Stephen Perle's poster discussing the the global media framing of the Chiropractic profession through the use of Google PageRank
Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
No full textWireless Sensor Networks (WSNs) have become essential components for a variety of environmental, surveillance, military, traffic control, and healthcare applications. These applications face critical challenges such as communication, security, power consumption, data aggregation, heterogeneities of sensor hardware, and Quality of Service (QoS) issues. Service-Oriented Architecture (SOA) is a software architecture that can be integrated with WSN applications to address those challenges. The SOA middleware bridges the gap between the high-level requirements of different applications and the hardware constraints of WSNs. This survey explores state-of-the-art approaches based on SOA and Service-Oriented Middleware (SOM) architecture that provide solutions for WSN challenges. The categories of this paper are based on approaches of SOA with and without middleware for WSNs. Additionally, features of SOA and middleware architectures for WSNs are compared to achieve more robust and efficient network performance. Design issues of SOA middleware for WSNs and its characteristics are also highlighted. The paper concludes with future research directions in SOM architecture to meet all requirements of emerging application of WSNs.https://doi.org/10.3390/s1703053
Unintentional Injury Related Deaths in USA
No full textSince 1991, unintentional injuries have ranked as the fifth leading cause of death behind heart disease, cancer, chronic lower respiratory disease, and stroke. As shown in the top graph on the facing page, deaths in the United States are dominated by heart disease and cancer. The next three causes, chronic lower respiratory disease, stroke, and unintentional injuries, look almost insignificant in comparison. These three causes combined account for fewer deaths than cancer alone. Given this reality, why are organizations like the National Safety Council focused on preventing unintentional injuries
Big Data Analytics in Supply Chain Management: A Literature Review on Supply Chain Analytics
No full textThe amount of the data produced by the government, the private sector and the general public has been rising especially over the past decade. With this growing trend, utilizing big data to add value to organizations became a popular topic for the industry and academic research. Converting unorganized and unstructured big data to useful information is investigated under Big Data Analytics (BDA). BDA, when employed appropriately, offers great potential to organizations helping in creating well-defined and meaningful strategic planning process. Supply Chain Analytics (SCA) is a member of BDA with a narrower spectrum, concerned exclusively with supply chain and logistics operations. SCA utilizes various Big Data Analytics techniques such as future trend analysis and prediction and/or operational optimization to increase the overall performance of related activities. This study presents a comprehensive literature survey in the area of Supply Chain Analytics and defines the literature gap in the related area
The Influence of Emotional States on Short-term Memory Retention by using Electroencephalography (EEG) Measurements: A Case Study
No full textThis study explored how emotions can impact short-term memory retention, and thus the process of learning, by analyzing five mental tasks. EEG measurements were used to explore the effects of three emotional states (e.g., neutral, positive, and negative states) on memory retention. The ANT Neuro system with 625Hz sampling frequency was used for EEG recordings. A public-domain library with emotion-annotated images was used to evoke the three emotional states in study participants. EEG recordings were performed while each participant was asked to memorize a list of words and numbers, followed by exposure to images from the library corresponding to each of the three emotional states, and recall of the words and numbers from the list. The ASA software and EEGLab were utilized for the analysis of the data in five EEG bands, which were Alpha, Beta, Delta, Gamma, and Theta. The frequency of recalled event-related words and numbers after emotion arousal were found to be significantly different when compared to those following exposure to neutral emotions. The highest average energy for all tasks was observed in the Delta activity. Alpha, Beta, and Gamma activities were found to be slightly higher during the recall after positive emotion arousal.https://doi.org/10.5220/000617140205021
An Optimal and Energy Efficient Multi-Sensor Collision-Free Path Planning Algorithm for a Mobile Robot in Dynamic Environments
No full textThere has been a remarkable growth in many different real-time systems in the area of autonomous mobile robots. This paper focuses on the collaboration of efficient multi-sensor systems to create new optimal motion planning for mobile robots. A proposed algorithm is used based on a new model to produce the shortest and most energy-efficient path from a given initial point to a goal point. The distance and time traveled, in addition to the consumed energy, have an asymptotic complexity of O(nlogn), where n is the number of obstacles. Real time experiments are performed to demonstrate the accuracy and energy efficiency of the proposed motion planning algorithm.https://doi.org/10.3390/robotics602000
Main Obstacles to Turkey's Accession to the European Union
No full textSince 1959, Turkish attempts to join the EU have failed. Several sets of talks have been held and many obstacles have risen, such as cultural, security, economic, and geopolitical barriers. Additionally, joining the EU is a significant challenge because it demands major changes in the Turkish socio-cultural identity to fulfill the EU standards; specifically, Turkey is primarily a Muslim country which is trying to be a part of a Christian union that has different norms, values, and cultures
Reducing Covariate Factors Of Gait Recognition Using Feature Selection, Dictionary-Based Sparse Coding, And Deep Learning
No full textHuman gait recognition is a behavioral biometrics method that aims to determine the identity of individuals through the manner and style of their distinctive walk. It is still a very challenging problem because natural human gait is affected by many covariate conditions such as changes in the clothing, variations in viewing angle, and changes in carrying condition. Although existing gait recognition methods perform well under a controlled environment where the gait is in normal condition with no covariate factors, the performance drastically decreases in practical conditions where it is susceptible to many covariate factors. In the first section of this dissertation, we analyze the most important features of gait under the carrying and clothing conditions. We find that the intra-class variations of the features that remain static during the gait cycle affect the recognition accuracy adversely. Thus, we introduce an effective and robust feature selection method based on the Gait Energy Image. The new gait representation is less sensitive to these covariate factors. We also propose an augmentation technique to overcome some of the problems associated with the intra-class gait fluctuations, as well as if the amount of the training data is relatively small. Finally, we use dictionary learning with sparse coding and Linear Discriminant Analysis (LDA) to seek the best discriminative data representation before feeding it to the Nearest Centroid classifier. When our method is applied on the large CASIA-B and OU-ISIR-B gait data sets, we are able to outperform existing gait methods. In addition, we propose a different method using deep learning to cope with a large number of covariate factors. We solve various gait recognition problems that assume the training data consist of diverse covariate conditions. Recently, machine learning based techniques have produced promising results for challenging classification problems. Since a deep convolutional neural network (CNN) is one of the most advanced machine learning techniques with the ability to approximate complex non-linear functions, we develop a specialized deep CNN architecture for gait recognition. The proposed architecture is less sensitive to several cases of the common variations and occlusions that affect and degrade gait recognition performance. It can also handle relatively small data sets without using any augmentation or fine-tuning techniques. Our specialized deep CNN model outperforms the existing gait recognition techniques when tested on the CASIA-B large gait dataset