Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness

This paper is concerned with flow control and resource allocation problems in computer networks in which real-time applications may have hard quality of service (QoS) requirements. Recent optimal flow control approaches are unable to deal with these problems since QoS utility functions generally do not satisfy the strict concavity condition in real-time applications. For elastic traffic, we show that bandwidth allocations using the existing optimal flow control strategy can be quite unfair. If we consider different QoS requirements among network users, it may be undesirable to allocate bandwidth simply according to the traditional max-min fairness or proportional fairness. Instead, a network should have the ability to allocate bandwidth resources to various users, addressing their real utility requirements. For these reasons, this paper proposes a new distributed flow control algorithm for multiservice networks, where the application's utility is only assumed to be continuously increasing over the available bandwidth. In this, we show that the algorithm converges, and that at convergence, the utility achieved by each application is well balanced in a proportionally (or max-min) fair manner

Microwave shielding of transparent and conducting single-walled carbon nanotube films

The authors measured the transport properties of single-walled carbon nanotube (SWCNT) films in the microwave frequency range from 10 MHz to 30 GHz by using the Corbino reflection technique from temperatures of 20-400 K. Based on the real and imaginary parts of the microwave conductivity, they calculated the shielding effectiveness for various film thicknesses. Shielding effectiveness of 43 dB at 10 MHz and 28 dB at 10 GHz are found for films with 90% optical transmittance, which suggests that SWCNT films are promising as a type of transparent microwave shielding material. By combining their data with those from the literature, the conductivity of SWCNT films was established in a broad frequency range from dc to visible.Comment: 4 pages, 4 figure

An Approach to Gain Score Dependability & Validity for Criterion-Referenced Language Tests

Much of the recent work on criterion-referenced language testing addresses the issues of item writing and cut score dependability. Cdterion-referenced item writing is centrally concemed with determining the content congruence and leamability of each item's content. Cut score dependability focuses on the consistency of decisions in repeated testing or the assessment of language leamer performances. A more general issue related to language program development also involves empirical rationalization of cut score decisions. In this case the issue is of determining the optimal index of gain score dependability in the pre-instruction and post-instruction approach to assessing the language learning gains. The present paper examines a commonly used approach to assessing gain score dependability. Thc optimal index of gain score dependability is derived from examining the cut score dependability of the pre-instructional administration of the criterion-referenced test as well as the postinstructional criterion-referenced test, in relation to differences in the ratio of pre and post instruction variances. The database for the present paper comes from a preinstruction administration of an academic Iistening test followed by a counterbalanced post-instruction administration of an altemate form of the same test after one semester of instruction. The subjects were 213 advanced ESL learners at a large American university English language institute

Tunability of Superconducting Metamaterials

Metamaterials are artificial structures with unique electromagnetic properties, such as relative dielectric permittivity and magnetic permeability with values less than 1, or even negative. Because these properties are so sensitive to loss, we have developed metamaterials comprised of superconducting waveguides, wires, and split-ring resonators. An important requirement for applications of these metamaterials is the ability to tune the frequency at which the unique electromagnetic response occurs. In this paper we present three methods (unique to superconductors) to accomplish this tuning: temperature, dc magnetic field, and rf magnetic field. Data are shown for dc and rf magnetic field tuning of a single Nb split-ring resonator (SRR). It was found that the dc field tuning was hysteritic in the resonant frequency data, while the quality factor, $Q$, was less hystertic. The rf power tuning showed no hysteresis, but did show supression of the $Q$ at high power. Magneto-optical images reveal inhomogeneous magnetic vortex entry in the dc field tuning, and laser scanning photoresponse images for a YBa$_2$Cu$_3$O$_{7-\delta}$ SRR reveals the current distribution in the rings.Comment: RexTEX, 4 pages of text with 6 figures plus 1/5 page of references, submitted for the 2006 Applied Superconductivity Conference; Revised edition: spelling corrections, and we removed mention of measuring the Current Density and replaced this with a more explicit definition of what we measure (with reference

A comparison between a two-feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

Highly-compliant elastic actuators have become progressively prominent over the last years for a variety of robotic applications. With remarkable shock tolerance, elastic actuators are appropriate for robots operating in unstructured environments. In accordance with this trend, a novel elastic actuator was recently designed by our research group for Serpens, a low-cost, open-source and highly-compliant multi-purpose modular snake robot. To control the newly designed elastic actuators of Serpens, a two-feedback loops position control algorithm was proposed. The inner controller loop is implemented as a model reference adaptive controller (MRAC), while the outer control loop adopts a fuzzy proportional-integral controller (FPIC). The performance of the presented control scheme was demonstrated through simulations. However, the efficiency of the proposed controller is dependent on the initial values of the parameters of the MRAC controller as well as on the effort required for a human to manually construct fuzzy rules. An alternative solution to the problem might consist of using methods that do not assume a priori knowledge: a solution that derives its properties from a machine learning procedure. In this way, the controller would be able to automatically learn the properties of the elastic actuator to be controlled. In this work, a novel controller for the proposed elastic actuator is presented based on the use of an artificial neural network (ANN) that is trained with reinforcement learning. The newly designed control algorithm is extensively compared with the former approach. Simulation results are presented for both methods. The authors seek to achieve a fair, non-biased, risk-aware and trustworthy comparison

A comparison between a two feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

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Flow control in networks with multiple paths

We propose two flow control algorithms for networks with multiple paths between each source-destination pair. Both are distributed algorithms over the network to maximize aggregate source utility. Algorithm 1 is a first order Lagrangian method applied to a modified objective function that has the same optimal solution as the original objective function but has a better convergence property. Algorithm 2 is based on the idea that, at optimality, only paths with the minimum price carry positive flows, and naturally decomposes the overall decision into flow control (determines total transmission rate based on minimum path price) and routing (determines how to split the flow among available paths). Both algorithms can be implemented as simply a source-based mechanism in which no link algorithm nor feedback is needed. We present numerical examples to illustrate their behavior