On Inter-referential Awareness in Collaborative Augmented Reality

For successful collaboration to occur, a workspace must support inter-referential awareness - or the ability for one participant to refer to a set of artifacts in the environment, and for that reference to be correctly interpreted by others. While referring to objects in our everyday environment is a straight-forward task, the non-tangible nature of digital artifacts presents us with new interaction challenges. Augmented reality (AR) is inextricably linked to the physical world, and it is natural to believe that the re-integration of physical artifacts into the workspace makes referencing tasks easier; however, we find that these environments combine the referencing challenges from several computing disciplines, which compound across scenarios. This dissertation presents our studies of this form of awareness in collaborative AR environments. It stems from our research in developing mixed reality environments for molecular modeling, where we explored spatial and multi-modal referencing techniques. To encapsulate the myriad of factors found in collaborative AR, we present a generic, theoretical framework and apply it to analyze this domain. Because referencing is a very human-centric activity, we present the results of an exploratory study which examines the behaviors of participants and how they generate references to physical and virtual content in co-located and remote scenarios; we found that participants refer to content using physical and virtual techniques, and that shared video is highly effective in disambiguating references in remote environments. By implementing user feedback from this study, a follow-up study explores how the environment can passively support referencing, where we discovered the role that virtual referencing plays during collaboration. A third study was conducted in order to better understand the effectiveness of giving and interpreting references using a virtual pointer; the results suggest the need for participants to be parallel with the arrow vector (strengthening the argument for shared viewpoints), as well as the importance of shadows in non-stereoscopic environments. Our contributions include a framework for analyzing the domain of inter-referential awareness, the development of novel referencing techniques, the presentation and analysis of our findings from multiple user studies, and a set of guidelines to help designers support this form of awareness

Kristen Paulsen Honors Portfolio

Kristen Paulsen\u27s honors portfolio captured in December 2014

Fish community response to the addtion of wood in Smith Creek, Virginia

Large woody debris (LWD) is an important component of healthy stream ecosystems. LWD shapes stream physical habitat features, captures and stores organic matter, and serves as shelter, feeding sites, and reproductive substrate for fish and other aquatic organisms. Loss of in-stream LWD and sources of LWD as a result of human activities has negatively impacted many streams. Recently, restoration of in-stream LWD and riparian sources of LWD have become important facets of stream restoration, though many projects involving LWD restoration lack monitoring and evaluation. I evaluated the effects of adding LWD on the fish community in Smith Creek, a small coolwater stream located in Northwest Virginia with a history of habitat degradation due to intense agricultural activity throughout the watershed. I added wooden pallets to previously established fish monitoring stream sections and employed a repeated measures design to examine changes in fish community metrics two years after wood addition. Analyses revealed a significant decrease in density of a dominant benthic species and increase in densitiy of mid-column fish species and a decrease in density of benthic fish species in complex wood treated sections. These results suggest possible interactions between increases in habitat complexity and fish communities in degraded streams. This research can provide valuable information about expected fish community responses to the restoration or addition of LWD in streams and could have implications for future management decisions involving riparian forest management and stream habitat restoration

MODIFICATION OF ALEXNET ARCHITECTURE FOR DETECTION OF CAR PARKING AVAILABILITY IN VIDEO CCTV

The difficulty of finding a parking space in public places, especially during peak hours is a problem experienced by drivers. To assist the driver in finding parking space availability, a system is needed to monitor parking availability. One study to detect the availability of parking lots utilizing CCTV. However, research on the availability of parking spaces on CCTV data has several problems, detecting parking slots that are done manually to be inefficient when applied to different parking lots. Also, research to detect the availability of parking lots using the Convolution Neural Network (CNN) method with existing architecture has many parameters. Therefore, this study proposes a system to detect the availability of car parking lots using You Only Look Once (YOLO) V3 for marking the parking space and proposed a new architecture CNN called Lite AlexNet which has few parameters than other methods to speed up the process of detecting parking space availability. The best accuracy of the marking stage using YOLO V3 is 92.31% where the weather was cloudy. For the proposed Lite AlexNet get the best time training average which is 7 second compare to other existing methods and the average accuracy in every condition is 92.33% better than other methods

Rancang Bangun Aplikasi Pendeteksi Suara Tangisan Bayi

Suara tangisan bayi merupakan sebuah tanda dari bayi yang mengalami suatu masalah. Namun, tidak semua orang dapat mengenali arti tangis bayi. Beberapa penelitian tentang deteksi suara tangis bayi sudah dilakukan oleh beberapa peneliti, namun saat ini masih belum ada penelitian yang membuat sebuah aplikasi pendeteksi suara tangis bayi berbasis web. Pada penelitian ini, sebuah aplikasi dibuat untuk membantu pengguna mengenali suara tangis bayi berbasis Dunstan Baby Language. Metode yang diterapkan adalah ekstraksi fitur suara tangis bayi dengan algoritma Mel-Frequency Cepstrum Coefficient (MFCC), normalisasi hasil ekstraksi fitur, dan klasifikasi K-nearest Neighbor. Dari berbagai pengujian yang dilakukan, dapat disimpulkan bahwa akurasi rata-rata terbaik sebesar 75,95% dapat dicapai ketika menggunakan parameter wintime pada ekstraksi fitur MFCC sebesar 0,08 detik, proporsi data latih 85% dan data uji 15% dari setiap kelas, normalisasi ekstraksi fitur dengan Standard Deviation Normalization, dan klasifikasi K-nearest Neighbor dengan k=1. Pada pengujian aplikasi dengan seluruh data, akurasi rata-rata yang sebesar 96,57% dapat dicapai ketika menggunakan parameter wintime pada ekstraksi fitur MFCC sebesar 0,08 detik, proporsi data latih 85% setiap kelas, normalisasi ekstraksi fitur dengan Standard Deviation Normalization, dan klasifikasi K-nearest Neighbor dengan k=1

An in-depth performance analysis of the oversampling techniques for high-class imbalanced dataset

Class imbalance occurs when the distribution of classes between the majority and the minority classes is not the same. The data on imbalanced classes may vary from mild to severe. The effect of high-class imbalance may affect the overall classification accuracy since the model is most likely to predict most of the data that fall within the majority class.  Such a model will give biased results, and the performance predictions for the minority class often have no impact on the model. The use of the oversampling technique is one way to deal with high-class imbalance, but only a few are used to solve data imbalance. This study aims for an in-depth performance analysis of the oversampling techniques to address the high-class imbalance problem. The addition of the oversampling technique will balance each class’s data to provide unbiased evaluation results in modeling. We compared the performance of Random Oversampling (ROS), ADASYN, SMOTE, and Borderline-SMOTE techniques. All oversampling techniques will be combined with machine learning methods such as Random Forest, Logistic Regression, and k-Nearest Neighbor (KNN). The test results show that Random Forest with Borderline-SMOTE gives the best value with an accuracy value of 0.9997, 0.9474 precision, 0.8571 recall, 0.9000 F1-score, 0.9388 ROC-AUC, and 0.8581 PRAUC of the overall oversampling technique

Determination of Location and Severity of Nodules on Lung Cancer CT Image Using YOLO Methods

The severity of lung cancer can be used to determine appropriate treatment measures and reduce the risk of death. The severity identification is monitored based on the size and location of the nodule. However, previous studies still focused on determining the location of nodules without identifying their severity. In this study, the severity of lung cancer is detected based on the size of its nodules. This research contributes to the annotation of severity to the Lung Image Database Consortium image collection (LIDC-IDRI) dataset and the development of automatic severity detection using You Only Look Once (YOLO) methods. The data is given a severity level based on the nodule size calculated based on the number of pixels in the nodule length. Automatic detection is done using YOLO methods, which consist of several versions, namely YOLOv5, YOLOv7, and YOLOv8. YOLO methods can properly detect the location and severity of cancer nodules with the IoU evaluation results obtained using YOLOv5, YOLOv7, and YOLOv8, which are 0.86, 0.6, and 0.87, respectively. From the experiment, it can be concluded that determining the location and severity of cancer based on nodule size using YOLO methods is proven effective and can be done in real-time

FACIAL INPAINTING IN UNALIGNED FACE IMAGES USING GENERATIVE ADVERSARIAL NETWORK WITH FEATURE RECONSTRUCTION LOSS

Facial inpainting or face restoration is a process to reconstruct some missing region on face images such that the inpainting results still can be seen as a realistic and original image without any missing region, in such a way that the observer could not realize whether the inpainting result is a generated or original image. Some of previous researches have done inpainting using generative network, such as Generative Adversarial Network. However, some problems may arise when inpainting algorithm have been done on unaligned face. The inpainting result show spatial inconsistency between the reconstructed region and its adjacent pixel, and the algorithm fail to reconstruct some area of face. Therefore, an improvement method in facial inpainting based on deep-learning is proposed to reduce the effect of the stated problem before, using GAN with additional loss from feature reconstruction and two discriminators. Feature reconstruction loss is a loss obtained by using pretrained network VGG-Net, Evaluation of the result shows that additional loss from feature reconstruction loss and two type of discriminators may help to increase visual quality of inpainting result, with higher PSNR and SSIM than previous result

A comparative study of finger vein recognition by using Learning Vector Quantization

Abstract¾ This paper presents a comparative study of finger vein recognition using various features with Learning Vector Quantization (LVQ) as a classification method. For the purpose of this study, two main features are employed: Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP). The other features that formed LEBP features: Local Multilayer Binary Pattern (LmBP) and Local Directional Binary Pattern (LdBP) are also employed. The type of images are also become the base of comparison. The SIFT features will be extracted from two types of images which are grayscale and binary images. The feature that have been extracted become the input for recognition stage. In recognition stage, LVQ classifier is used. LVQ will classify the images into two class which are the recognizable images and non recognizable images. The accuracy, false positive rate (FPR), and true positive rate (TPR) value are used to evaluate the performance of finger vein recognition. The performance result of finger vein recognition becomes the main study for comparison stage. From the experiments result, it can be found which feature is the best for finger vein reconition using LVQ. The performance of finger vein recognition that use SIFT feature from binary images give a slightly better result than uisng LmBP, LdBP, or LEBP feature. The accuracy value could achieve 97,45%, TPR at 0,9000 and FPR at 0,0129.