Towards Efficient Multi-Codec Streaming

The importance of hard-ware-in-the-loop infrared seekers testing with realistic infrared scenes warrants a review of the current technologies used in dynamic infrared scene simulation. Infrared seeker dynamic scene simulation technology includes dynamic infrared simulator technology and infrared scene image generation technology. Infrared simulator includes resistor arrays, liquid crystal light valves, laser writers, laser diode arrays, CRT and digital mirror devices (DMD). The technology that may provide high performance for testing seekers is the DMD approach. Other technologies such as resistor arrays, LCLVs, laser diodes, and the CRTs are still under development. Infrared scene generation can be divided into the stage of data generation and the stage of programming. Target 3D model is completed by the software of Multigen Creator and dynamic scene image generation is completed by the software of Vega. The SensorVision module of Vega can supply the atmosphere model parameter and target texture material.

Many datasets, including market basket data, text or hypertext documents, and measurement data collected in different nodes or time periods, are modeled as a collection of sets over a ground set of (weighted) items. We consider the problem of estimating basic aggregates such as the weight or selectivity of a subpopulation of the items. We extend classic summarization techniques based on sampling to this scenario when we have multiple sets and selection predicates based on membership in particular sets.

Traditional mechanical design relies on iterative refinement cycles driven by expert heuristics and computationally expensive Finite Element Method (FEM) analyses to meet performance targets. While machine learning approaches have been developed to automate portions of this process, they typically demand large datasets, substantial computational resources, and remain narrowly tailored to specific domains, thereby limiting their generalizability across diverse design problems. We propose a framework coupling a pretrained Large Language Model (LLM) with an FEM module to autonomously generate, evaluate, and refine structural designs based on performance specifications and quantitative feedback. Without domain-specific fine-tuning, the LLM leverages general reasoning to iteratively propose candidate structures, interpret simulation results, and implement mechanically informed modifications. Demonstrated on 2D truss structures, the approach navigates complex design spaces, balances competing objectives, and identifies appropriate stopping points when optimization yields diminishing returns. Compared to traditional methods like NSGA-II, LLM-guided optimization achieves faster convergence with fewer FEM evaluations in highly discrete, multi-faceted design spaces involving dynamic node generation and discrete member sizing. Experiments across multiple temperature settings and model scales reveal that smaller models achieve slightly better constraint satisfaction with fewer iterations, while lower temperatures produce more consistent performance. These findings position LLM agents as a promising new class of natural language-based, reasoning-driven optimizers for engineering design.

The proposed work concentrates on the networking facets of sensor-cloud infrastructures—one of the first attempts of its kind. In a sensor-cloud, multiple sets of physical sensor nodes that are activated based on an application demand, in turn give rise to multiple distinct virtual sensors (VSs). The VSs are considered to span across multiple geographical regions; thereby, depositing the data (from each of the VS) to the closest cloud data center (DC). Quite obviously, multiple geospatial DCs get involve with an application. However, the principle of sensor-cloud is to store and conglomerate the data from various VSs, before they can be provisioned as Sensors-as-a-Service (Se-aaS). The assortation of data occurs within a single Virtual Machine (VM) (or in some cases multiple VMs) residing inside a particular DC. This work addresses the problem of scheduling a particular DC that congregates data from various VSs, and transmit the same to the end-user application. The work follows the general pairwise choice framework of the Optimal Decision Rule. The scheduling of the DC is performed under several network constraints, such as data migration cost, data delivery cost, and service delay of an application that ensures the preservation of the Quality-of-Service (QoS) and maintenance of the user satisfaction. The work quantifies the effective QoS of Se-aaS and determines an optimal decision rule for electing a particular DC. While arriving at a collective decision, the work incorporates the fallible decision making ability of a DC; thereby, excluding the loss of generality. Experimental results depict that the proposed algorithm for generating the optimal decision rule finds applicability in real-time cloud computing scenarios.

the hyperbolic paraboloid is a kind of quadric surface and ruled surface. In this paper, we take the generation process of the rotating surface as introduction. Then we select multiple sets of parallel planes to intercept the hyperbolic paraboloid based on use the cross section method, and then discuss the graph and drawing method of the hyperbolic paraboloid. Meanwhile, we certified the ruled surface features by simulating the dynamic generation process. When the hyperbolic parabolic thin shell is used as the roof, it has the advantages of beautiful shape, good force performance and easy construction. So it has wide application in engineering construction. We explore the shape of the roof of Xinghai Concert Hall by analyzing the plane truncation. Finally, we use MATLAB to simulate its dynamic construction process.