The expeditious improvement in medical imaging technology has been crucial in diagnosing various conditions like cervical spondylosis. However, there is a need for improvement in terms of accuracy and efficiency in the existing models to obtain optimal diagnostic results. This limitation of existing models particularly hampers the resolution and clarity of MRI where there is a need for finer details for the accurate diagnoses of the problem. To limit this gap, our research represents a pioneering approach that merges GAN and spectral clustering. Our research shows the innovative amalgamation of two technologies. The GAN model is enhanced by the sturdy segmentation abilities of spectral clustering, resulting in the significant betterment in diagnosis of problems. This GAN is specifically designed for medical imaging; it consists of a deep convolutional network based on U‐Net architecture. GAN consists of a generator that generates the MRI image through a series of convolutional and deconvolutional layers, and a discriminator checks whether the MRI image is real or generated. This approach not only improves the quality of the image but also leads to a more brisk and accurate diagnosis of cervical spine deformities. The methodology was meticulously tested on diverse datasets, including Medscape, RSNA 2022, and CTSpine1k. The results were remarkable, showing an 8.3% increase in accuracy, 5.5% improvement in precision, 8.5% higher recall, 3.5% greater AUC, 4.9% increased specificity, and a 1.9% reduction in delay compared to the existing classification methods. The influence of this work is profound, providing a consideration spike in the capability of diagnosing problems of cervical spondylosis. By providing improved image resolution and highly precise diagnostic tools, this advancement helps clinicians to make more accurate decisions as well as provides various innovations that help in medical imaging in the future.

The next-generation social communication medium, tele-immersion, is receiving increasing attention from both the research and industrial communities. It enables interaction between geographically distributed sites through realistic reconstruction of physical scenes in immersive spaces. While the previous research focused on real-world measurements of implemented tele-immersive systems, we take a model-based approach to study the system more generally and thoroughly. With a model validated with actual measurements, we evaluate the system performance in a larger scale, and understand the quantitative effect of the algorithmic enhancements that were proposed in the literature. We also predict futuristic performance characteristic and show that high interactivity can be foreseen with hardware advances in the near future.

This paper presents (a) new technologies for real-time immersion of humans into virtual reality (VR) environments with non-invasive real-time three-dimensional (3D) imaging, (b) a new methodology for evaluating immersive VR spaces in rehabilitation applications, and (c) experimental results documenting the benefits of Immersive VR (IVR) spaces for regaining proprioception. Our work focuses on designing immersive VR spaces with non-invasive multimedia sensory inputs where real time digital clones of humans are fused with virtual scenes for rehabilitation purposes. We hypothesize that humans with proprioceptive impairments can use their other senses as the proprioceptive feedback from real-time 3D+color reconstructions of their bodies in space. The objective is to investigate this hypothesis and quantify any benefits of immersive environments for regaining proprioception as one example of a rehabilitation application. The paper describes (a) the portable immersive VR system for real time 3D imaging, reconstruction and rendering, (b) a new methodology for quantitative evaluations of rehabilitation experiments in immersive VR spaces, and (c) the experimental results obtained for validating the above hypothesis with wheelchair basketball athletes. The novelty of the work lies in the first of its kind evaluation of the benefits of immersive VR spaces with multimedia cues for regaining proprioception.

With the growing proliferation of conversational media and devices for generating multimedia content, the Internet has seen an expansion in websites catering to user-generated media. Most of the user-generated content is multimodal in nature as it has videos, audio, text (in the form of tags), comments and so on. Content analysis is a challenging problem on this type of media since it is noisy, unstructured and unreliable. In this paper we propose VideoMule, a consensus learning approach for multi-label video classification from noisy user-generated videos. In our scheme, we train classification and clustering algorithms on individual modes of information such as user comments, tags, video features and so on. We then combine the results of trained classifiers and clustering algorithms using a novel heuristic consensus learning algorithm which as a whole performs better than each individual learning model.

The paper introduces the application of IEEE 802.11e to the delivery of high bandwidth multimedia streams. The architecture for a QoS enabled MAC is described along with an efficient scheduling algorithm, which, when deployed at the AP, is able to serve QoS sensitive streams, while making efficient use of the channel bandwidth. The 802.11e MAC is simulated using OPNET and the performance of both the EDCA (enhanced distributed channel access) and the HCCA (HCF controlled channel access) mechanisms is studied under various scenarios. The results illustrate that HCCA when used in conjunction with the QoS scheduler satisfies the requirements of AV and speech applications.