Santa Fe Traffic Operation Center

The New Mexico Department of Transportation is expanding the District 5 Traffic Operations Center (TOC). We assessed the current communication methods and technologies used within the District 5 TOC. From various interviews and research we recommended that the TOC increase operational efficiency and interagency communications by implementing the following technologies and systems: Dashboard systems, NMRoads access expansion, automatic vehicle location, jurisdictional boundaries and infrastructure map, and interagency conferences, notification systems and contact lists

remote laboratory experiments in a virtual immersive learning environment

TheVirtual Immersive Learning(VIL) test bench implements a virtual collaborative immersive environment, capable of integrating natural contexts and typical gestures, which may occur during traditional lectures, enhanced with advanced experimental sessions. The system architecture is described, along with the motivations, and the most significant choices, both hardware and software, adopted for its implementation. The novelty of the approach essentially relies on its capability of embedding functionalities that stem from various research results (mainly carried out within the VICOM national project), and "putting the pieces together" in a well-integrated framework. These features, along with its high portability, good flexibility, and, above all, low cost, make this approach appropriate for educational and training purposes, mainly concerning measurements on telecommunication systems, at universities and research centers, as well as enterprises. Moreover, the methodology can be employed for remote access to and sharing of costly measurement equipment in many different activities. The immersive characteristics of the framework are illustrated, along with performance measurements related to a specific application

A Survey of Smart Classroom Literature

Recently, there has been a substantial amount of research on smart classrooms, encompassing a number of areas, including Information and Communication Technology, Machine Learning, Sensor Networks, Cloud Computing, and Hardware. Smart classroom research has been quickly implemented to enhance education systems, resulting in higher engagement and empowerment of students, educators, and administrators. Despite decades of using emerging technology to improve teaching practices, critics often point out that methods miss adequate theoretical and technical foundations. As a result, there have been a number of conflicting reviews on different perspectives of smart classrooms. For a realistic smart classroom approach, a piecemeal implementation is insufficient. This survey contributes to the current literature by presenting a comprehensive analysis of various disciplines using a standard terminology and taxonomy. This multi-field study reveals new research possibilities and problems that must be tackled in order to integrate interdisciplinary works in a synergic manner. Our analysis shows that smart classroom is a rapidly developing research area that complements a number of emerging technologies. Moreover, this paper also describes the co-occurrence network of technological keywords using VOSviewer for an in-depth analysis

Design Of Computer Vision Systems For Optimizing The Threat Detection Accuracy

This dissertation considers computer vision (CV) systems in which a central monitoring station receives and analyzes the video streams captured and delivered wirelessly by multiple cameras. It addresses how the bandwidth can be allocated to various cameras by presenting a cross-layer solution that optimizes the overall detection or recognition accuracy. The dissertation presents and develops a real CV system and subsequently provides a detailed experimental analysis of cross-layer optimization. Other unique features of the developed solution include employing the popular HTTP streaming approach, utilizing homogeneous cameras as well as heterogeneous ones with varying capabilities and limitations, and including a new algorithm for estimating the effective medium airtime. The results show that the proposed solution significantly improves the CV accuracy. Additionally, the dissertation features an improved neural network system for object detection. The proposed system considers inherent video characteristics and employs different motion detection and clustering algorithms to focus on the areas of importance in consecutive frames, allowing the system to dynamically and efficiently distribute the detection task among multiple deployments of object detection neural networks. Our experimental results indicate that our proposed method can enhance the mAP (mean average precision), execution time, and required data transmissions to object detection networks. Finally, as recognizing an activity provides significant automation prospects in CV systems, the dissertation presents an efficient activity-detection recurrent neural network that utilizes fast pose/limbs estimation approaches. By combining object detection with pose estimation, the domain of activity detection is shifted from a volume of RGB (Red, Green, and Blue) pixel values to a time-series of relatively small one-dimensional arrays, thereby allowing the activity detection system to take advantage of highly capable neural networks that have been trained on large GPU clusters for thousands of hours. Consequently, capable activity detection systems with considerably fewer training sets and processing hours can be built

An alternative approach for assessing drug induced seizures, using non-protected larval zebrafish

As many as 9% of epileptic seizures occur as a result of drug toxicity. Identifying compounds with seizurogenic side effects is imperative for assessing compound safety during drug development, however, multiple marketed drugs still have clinical associations with seizures. Moreover, current approaches for assessing seizurogenicity, namely rodent EEG and behavioural studies, are highly resource intensive. This being the case, alternative approaches have been postulated for assessing compound seizurogenicity, including in vitro, in vivo, and in silico methods. In this thesis, experimental work is presented supporting the use of larval zebrafish as a candidate model organism for developing new seizure liability screening approaches. Larval zebrafish are translucent, meaning they are highly amenable to imaging approaches while offering a more ethical alternative to mammalian research. Zebrafish are furthermore highly fecund facilitating capacity for both high replication and high throughput. The primary goal of this thesis was to identify biomarkers in larval zebrafish, both behavioural and physiological, of compounds that increase seizure liability. The efficacy of this model organism for seizure liability testing was assessed through exposure of larval zebrafish to a mechanistically diverse array of compounds, selected for their varying degrees of seizurogenicity. Their central nervous systems were monitored using a variety of different techniques including light sheet microscopy, local field potential recordings, and behavioural monitoring. Data acquired from these measurements were then analysed using a variety of techniques including frequency domain analysis, clustering, functional connectivity, regression, and graph theory. Much of this analysis was exploratory in nature and is reflective of the infancy of the field. Experimental findings suggest that larval zebrafish are indeed sensitive to a wide range of pharmacological mechanisms of action and that drug actions are reflected by behavioural and direct measurements of brain activity. For example, local field potential recordings revealed electrographic responses akin to pre-ictal, inter-ictal and ictal events identified in humans. Ca2+ imaging using light sheet microscopy found global increases in fluorescent intensity and functional connectivity due to seizurogenic drug administration. In addition, [2] further functional connectivity and graph analysis revealed macroscale network changes correlated with drug seizurogenicity and mechanism of action. Finally, analysis of swimming behaviour revealed a strong correlation between high speed swimming behaviours and administration of convulsant compounds. In conclusion, presented herein are data demonstrating the power of functional brain imaging, LFP recordings, and behavioral monitoring in larval zebrafish for assessing the action of neuroactive drugs in a highly relevant vertebrate model. These data help us to understand the relevance of the 4 dpf larval zebrafish for neuropharmacological studies and reveal that even at this early developmental stage, these animals are highly responsive to a wide range of neuroactive compounds across multiple primary mechanisms of action. This represents compelling evidence of the potential utility of larval zebrafish as a model organism for seizure liability testing

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

Dynamic adaptation of streamed real-time E-learning videos over the internet

Even though the e-learning is becoming increasingly popular in the academic environment, the quality of synchronous e-learning video is still substandard and significant work needs to be done to improve it. The improvements have to be brought about taking into considerations both: the network requirements and the psycho- physical aspects of the human visual system. One of the problems of the synchronous e-learning video is that the head-and-shoulder video of the instructor is mostly transmitted. This video presentation can be made more interesting by transmitting shots from different angles and zooms. Unfortunately, the transmission of such multi-shot videos will increase packet delay, jitter and other artifacts caused by frequent changes of the scenes. To some extent these problems may be reduced by controlled reduction of the quality of video so as to minimise uncontrolled corruption of the stream. Hence, there is a need for controlled streaming of a multi-shot e-learning video in response to the changing availability of the bandwidth, while utilising the available bandwidth to the maximum. The quality of transmitted video can be improved by removing the redundant background data and utilising the available bandwidth for sending high-resolution foreground information. While a number of schemes exist to identify and remove the background from the foreground, very few studies exist on the identification and separation of the two based on the understanding of the human visual system. Research has been carried out to define foreground and background in the context of e-learning video on the basis of human psychology. The results have been utilised to propose methods for improving the transmission of e-learning videos. In order to transmit the video sequence efficiently this research proposes the use of Feed- Forward Controllers that dynamically characterise the ongoing scene and adjust the streaming of video based on the availability of the bandwidth. In order to satisfy a number of receivers connected by varied bandwidth links in a heterogeneous environment, the use of Multi-Layer Feed-Forward Controller has been researched. This controller dynamically characterises the complexity (number of Macroblocks per frame) of the ongoing video sequence and combines it with the knowledge of availability of the bandwidth to various receivers to divide the video sequence into layers in an optimal way before transmitting it into network. The Single-layer Feed-Forward Controller inputs the complexity (Spatial Information and Temporal Information) of the on-going video sequence along with the availability of bandwidth to a receiver and adjusts the resolution and frame rate of individual scenes to transmit the sequence optimised to give the most acceptable perceptual quality within the bandwidth constraints. The performance of the Feed-Forward Controllers have been evaluated under simulated conditions and have been found to effectively regulate the streaming of real-time e-learning videos in order to provide perceptually improved video quality within the constraints of the available bandwidth