In the Inertial Confinement Fusion (ICF) process, roughly a 2mm spherical shell made of high-density carbon is used as a target for laser beams, which compress and heat it to energy levels needed for high fusion yield in nuclear fusion. These shells are polished meticulously to meet the standards for a fusion shot. However, the polishing of these shells involves multiple stages, with each stage taking several hours. To make sure that the polishing process is advancing in the right direction, we are able to measure the shell surface roughness. This measurement, however, is very labor-intensive, time-consuming, and requires a human operator. To help improve the polishing process we have released the first dataset to the public that consists of raw vibration signals with the corresponding polishing surface roughness changes. We show that this dataset can be used with a variety of neural network based methods for prediction of the change of polishing surface roughness, hence eliminating the need for the time-consuming manual process. This is the first dataset of its kind to be released in public and its use will allow the operator to make any necessary changes to the ICF polishing process for optimal results. This dataset contains the raw vibration data of multiple polishing runs with their extracted statistical features and the corresponding surface roughness values. Additionally, to generalize the prediction models to different polishing conditions, we also apply domain adaptation techniques to improve prediction accuracy for conditions unseen by the trained model. The dataset is available in \url{https://junzeliu.github.io/Diamond-Polishing-Dataset/}.

Many existing algorithms manage network handover using static thresholds or weights applied to performance metrics. Such approaches are performance limited as they require knowledge of prior network performance. Performance metric thresholds and weights are often preconfigured based on the experience of network personnel. Static weightings are often configured for ideal network scenarios and are not able to adapt to changing environmental conditions. Previous studies have illustrated how the combination of foliage and weather can introduce interference at the receiver. This paper proposes an Algorithm for Mitigating ENvironmental Degradations. (AMEND). AMEND is a pluggable directed feed-forward neural network designed for vehicular environments. The handover decisions implemented by AMEND are based on predicted weather conditions, historic network performance and dynamic performance characteristics. Results illustrate that in varying weather conditions AMEND has improved overall performance over existing approaches. In poor weather conditions, implementation of AMEND has led to a performance improvement of over 500% in comparison to existing approaches.

Handover algorithms typically operate by assigning preconfigured threshold or weight values onto network performance metrics such as delay, data loss and signal strength. Such approaches are performance limited as they do not consider external factors that affect the network such as the physical environment and current weather conditions. Previous research illustrates that foliage density combined with detrimental weather conditions can have degrading effects on wireless links. The changes to these environmental factors over long vehicular-based mobile user sessions can lead to sub-optimal handover decisions and a negative impact on a user's Quality of Experience during mobile video streaming. There is need for a handover approach that adapts to these factors and mitigates any negative effects that occur. This paper proposes a method for Environmental factor Mitigation on mULtimedia StreamIng Networks (EMULSIoN). EMULSIoN uses a perceptron artificial neural network approach to mitigate the latency and delays caused by environmental factors. Using dynamic network performance metrics and with known topographical data, the EMULSIoN directed learning approach can learn from previous user sessions to mitigate these environmental effects. EMULSIoN further uses GPS and topographical data to divide vehicular routes into small sub-areas for optimal performance in varied terrain. Results illustrate that EMULSIoN has significant video quality improvements in comparison to pre-configured weight handover strategies.

In this paper we explore the effect of using augmented reality (AR) for three-dimensional graph link analysis. Two experiments were conducted. The first was designed to compare a tangible AR interface to a desktop-based interface. Different modes of viewing network graphs were presented using a variety of interfaces. The results of the first experiment show that a tangible AR interface is well suited to link analysis. The second experiment was designed to test the effect of stereographic viewing on graph comprehension. The results show that stereographic viewing has little effect on comprehension and performance. These experiments add support to the work of Ware and Frank, whose studies showed that depth and motion cues provide huge gains in spatial comprehension and accuracy in link analysis.

We conducted two experiments comparing communication behaviors of co-located users in collaborative augmented reality (AR) interfaces. In the first experiment, we compared optical, stereo- and mono-video, and immersive head mounted displays (HMDs) using a target identification task. It was found that differences in the real world visibility severely affect communication behaviors. The optical see-through case produced the best results with the least extra communication needed. Generally, the more difficult it was to use non-verbal communication cues, the more people resorted to speech cues to compensate. In the second experiment, we compared three different combinations of task and communication spaces using a 2D icon design task with optical see-through HMDs. It was found that the spatial relationship between the task and communication spaces also severely affected communication behaviors. Placing the task space between the subjects produced the most active behaviors in terms of initiatory body languages and utterances with least miscommunications.

Abstract This paper presents a novel automatic method for view synthesis (or image transfer) from a triplet of uncalibrated images based on trinocular edge matching followed by transfer by interpolation, occlusion detection and correction and finally rendering. The edge-based technique proposed here is of general practical relevance because it does not rely upon dense correspondences and is computationally efficient. Applications range from immersive media and teleconferencing, image interpolation for fast rendering and compression.

Article View synthesis by edge transfer with application to the generation of immersive video objects Share on Authors: Stephen Pollard Hewlett-Packard Laboratories, Filton Road, StokeGifford, Bristol, BS34 8QZ, UK Hewlett-Packard Laboratories, Filton Road, StokeGifford, Bristol, BS34 8QZ, UKView Profile , Sean Hayes Hewlett-Packard Laboratories, Filton Road, StokeGifford, Bristol, BS34 8QZ, UK Hewlett-Packard Laboratories, Filton Road, StokeGifford, Bristol, BS34 8QZ, UKView Profile Authors Info & Claims VRST '98: Proceedings of the ACM symposium on Virtual reality software and technologyNovember 1998 Pages 91–98https://doi.org/10.1145/293701.293713Online:02 November 1998Publication History 7citation302DownloadsMetricsTotal Citations7Total Downloads302Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

This paper presents a novel automatic method for view synthesis (or image transfer) from a triplet of uncalibrated images based on trinocular edge matching followed by transfer by interpolation, occlusion detection and correction and finally rendering. The edge-based technique proposed here is of general practical relevance because it overcomes most of the problems encountered in other approaches that either rely upon dense correspondence, work in projective space or need explicit camera calibration. Applications range from immersive media and teleconferencing, image interpolation for fast rendering and compression.