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1. Experimental Evaluation of Large Scale WiFi Multicast Rate Control

   Varun Gupta, Craig Gutterman (January 2018, IEEE transactions on wireless communications)

   WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality. 

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2. Machine learning enables precise holographic characterization of colloidal materials in real time

   https://doi.org/10.1039/D2SM01283A  

   Altman, Lauren E.; Grier, David G. (April 2023, Soft Matter)

   Holographic particle characterization uses in-line holographic video microscopy to track and characterize individual colloidal particles dispersed in their native fluid media. Applications range from fundamental research in statistical physics to product development in biopharmaceuticals and medical diagnostic testing. The information encoded in a hologram can be extracted by fitting to a generative model based on the Lorenz–Mie theory of light scattering. Treating hologram analysis as a high-dimensional inverse problem has been exceptionally successful, with conventional optimization algorithms yielding nanometer precision for a typical particle's position and part-per-thousand precision for its size and index of refraction. Machine learning previously has been used to automate holographic particle characterization by detecting features of interest in multi-particle holograms and estimating the particles' positions and properties for subsequent refinement. This study presents an updated end-to-end neural-network solution called CATCH (Characterizing and Tracking Colloids Holographically) whose predictions are fast, precise, and accurate enough for many real-world high-throughput applications and can reliably bootstrap conventional optimization algorithms for the most demanding applications. The ability of CATCH to learn a representation of Lorenz–Mie theory that fits within a diminutive 200 kB hints at the possibility of developing a greatly simplified formulation of light scattering by small objects. 

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3. A Measurement-Derived Functional Model for the Interaction Between Congestion Control and QoE in Video Conferencing

   He, Jia; Ammar, Mostafa; Zegura, Ellen (March 2023, Springer)

   Video Conferencing Applications (VCAs) that support remote work and education have increased in use over the last two years, contributing to Internet bandwidth usage. VCA clients transmit video and audio to each other in peer-to-peer mode or through a bridge known as a Selective Forwarding Unit (SFU). Popular VCAs implement congestion control in the application layer over UDP and accomplish rate adjustment through video rate control, ultimately affecting end user Quality of Experience(QoE). Researchers have reported on the throughput and video metric performance of specific VCAs using structuredexperiments. Yet prior work rarely examines the interaction between congestion control mechanisms and rate adjustment techniques that produces the observed throughput and QoE metrics. Understanding this interaction at a functional level paves the way to explain observed performance, to pinpoint commonalities and key functional differences across VCAs, and to contemplate opportunities for innovation. To that end, we first design and conduct detailed measurements of three VCAs(WebRTC/Jitsi, Zoom, Blue Jeans) to develop understanding of their congestion and video rate control mechanisms. We then use the measurement results to derive our functional models for the VCA client and SFU. Our models reveal the complexity of these systems and demonstrate how, despite some uniformity in function deployment, there is significant variability among the VCAs in the implementation of these functions. 

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4. Optical simulations for determining efficacy of new light source designs for excitation-scanning high-speed hyperspectral imaging systems

   https://doi.org/10.1117/12.2545201  

   Browning, Craig M.; Deal, Joshua; Gunn Mayes, Samantha; Parker, Marina; Rich, Thomas C.; Leavesley, Silas J.; Maitland, Kristen C.; Roblyer, Darren M.; Campagnola, Paul J. (March 2020, Proc. SPIE 11216, Multiscale Imaging and Spectroscopy)

   Positive outcomes for colorectal cancer treatment have been linked to early detection. The difficulty in detecting early lesions is the limited contrast with surrounding mucosa and minimal definitive markers to distinguish between hyperplastic and carcinoma lesions. Colorectal cancer is the 3rd leading cancer for incidence and mortality rates which is potentially linked to missed early lesions which allow for increased growth and metastatic potential. One potential technology for early-stage lesion detection is hyperspectral imaging. Traditionally, hyperspectral imaging uses reflectance spectroscopic data to provide a component analysis, per pixel, of an image in fields such as remote sensing, agriculture, food processing and archaeology. This work aims to acquire higher signal-to-noise fluorescence spectroscopic data, harnessing the autofluorescence of tissue, adding a hyperspectral contrast to colorectal cancer detection while maintaining spatial resolution at video-rate speeds. We have previously designed a multi-furcated LED-based spectral light source to prove this concept. Our results demonstrated that the technique is feasible, but the initial prototype has a high light transmission loss (~98%) minimizing spatial resolution and slowing video acquisition. Here, we present updated results in developing an optical ray-tracing model of light source geometries to maximize irradiance throughput for excitation-scanning hyperspectral imaging. Results show combining solid light guide branches have a compounding light loss effect, however, there is potential to minimize light loss through the use of optical claddings. This simulation data will provide the necessary metrics to verify and validate future physical optical components within the hyperspectral endoscopic system for detecting colorectal cancer. 

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5. Image and Video Tokenization with Binary Spherical Quantization

   Zhao, Yue; Xiong, Yuanjun; Krähenbühl, Philipp (June 2024, https://doi.org/10.48550/arXiv.2406.07548)

   This work introduces a transformer-based image and video tokenizer leveraging Binary Spherical Quantization (BSQ). The method projects high-dimensional visual embeddings onto a lower-dimensional hypersphere followed by binary quantization. BSQ offers three key benefits: (1) parameter efficiency without requiring an explicit codebook, (2) scalability to arbitrary token dimensions, and (3) high compression capability—up to 100× compression of visual data with minimal distortion. The tokenizer architecture includes a transformer encoder-decoder with block-wise causal masking to handle variable-length video inputs. The resulting model, BSQ-ViT, achieves state-of-the-art visual reconstruction performance on image and video benchmarks while delivering 2.4× higher throughput compared to previous best methods. Additionally, BSQ-ViT supports video compression via autoregressive priors for adaptive arithmetic coding, achieving results comparable to leading video compression standards. Furthermore, it enables masked language models to achieve competitive image synthesis quality relative to GAN- and diffusion-based approaches. 

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