Устройство для перемещения датчиков в магнитном поле малогабаритного бетатрона

Рассматривается возможность увеличения точности измерений характеристик магнитного поля посредством более точной установки датчиков в исследуемой точке

Joint Encoder And Vbr Channel Optimization With Buffer And Leaky Bucket Constraints

this paper we introduce the concept of effective buffer size which establishes the link between the channel rate and the buffer size required to prevent data loss in VBR transmission. Using the effective buffer size makes it simple to use our knowledge of the CBR case to better understand the VBR case. Given the constraints imposed by the effective buffer size and the network policing function, we introduce new techniques that allow joint optimization of the choice of encoder and channel rates. We focus on optimal techniques that will provide bounds on achievable performance and can also be used in off-line compression environments. Based on our results we can derive conclusions that are applicable to simpler coding scenario

Worst Case Arrivals of Leaky Bucket Constrained Sources: The Myth of the On-Off source

We have simulated a set of independent connections limited by leaky bucket shapers and fed into a buffered multiplexer. This scenario is typical of an ATM switch or in a looser sense of an RSVP capable router. We found periodic traffic patterns which result in much worse loss rates than the on-off or tristate patterns found in literature to date. We give an intuitive justification for what we believe is the worst case and back this with an extensive set of simulations. Our results are important for Connection Acceptance Control when connections are known to be statistically independent. They clearly invalidate the widespread belief that on-off patterns are the worst case traffic of independent leaky bucket constrained sources

Dynamic Bandwidth Allocation Based on Online Traffic Prediction for Real-Time MPEG-4 Video Streams

The distinct characteristics of variable bit rate (VBR) video traffic and its quality of service (QoS) constraints have posed a unique challenge on network resource allocation and management for future integrated networks. Dynamic bandwidth allocation attempts to adaptively allocate resources to capture the burstiness of VBR video traffic, and therefore could potentially increase network utilization substantially while still satisfying the desired QoS requirements. We focus on prediction-based dynamic bandwidth allocation. In this context, the multiresolution learning neural-network-based traffic predictor is rigorously examined. A well-known-heuristic based approach RED-VBR scheme is used as a baseline for performance evaluation. Simulations using real-world MPEG-4 VBR video traces are conducted, and a comprehensive performance metrics is presented. In addition, a new concept of renegotiation control is introduced and a novel renegotiation control algorithm based on binary exponential backoff (BEB) is proposed to efficiently reduce renegotiation frequency