Dynamic Server Allocation over Time Varying Channels with Switchover Delay

We consider a dynamic server allocation problem over parallel queues with randomly varying connectivity and server switchover delay between the queues. At each time slot the server decides either to stay with the current queue or switch to another queue based on the current connectivity and the queue length information. Switchover delay occurs in many telecommunications applications and is a new modeling component of this problem that has not been previously addressed. We show that the simultaneous presence of randomly varying connectivity and switchover delay changes the system stability region and the structure of optimal policies. In the first part of the paper, we consider a system of two parallel queues, and develop a novel approach to explicitly characterize the stability region of the system using state-action frequencies which are stationary solutions to a Markov Decision Process (MDP) formulation. We then develop a frame-based dynamic control (FBDC) policy, based on the state-action frequencies, and show that it is throughput-optimal asymptotically in the frame length. The FBDC policy is applicable to a broad class of network control systems and provides a new framework for developing throughput-optimal network control policies using state-action frequencies. Furthermore, we develop simple Myopic policies that provably achieve more than 90% of the stability region. In the second part of the paper, we extend our results to systems with an arbitrary but finite number of queues.Comment: 38 Pages, 18 figures. arXiv admin note: substantial text overlap with arXiv:1008.234

Competing interactions in artificial spin chains

The low-energy magnetic configurations of artificial frustrated spin chains are investigated using magnetic force microscopy and micromagnetic simulations. Contrary to most studies on two-dimensional artificial spin systems where frustration arises from the lattice geometry, here magnetic frustration originates from competing interactions between neighboring spins. By tuning continuously the strength and sign of these interactions, we show that different magnetic phases can be stabilized. Comparison between our experimental findings and predictions from the one-dimensional Anisotropic Next-Nearest-Neighbor Ising (ANNNI) model reveals that artificial frustrated spin chains have a richer phase diagram than initially expected. Besides the observation of several magnetic orders and the potential extension of this work to highly-degenerated artificial spin chains, our results suggest that the micromagnetic nature of the individual magnetic elements allows observation of metastable spin configurations.Comment: 5 pages, 4 figure

Analysis of two-dimensional high-energy photoelectron momentum distributions in single ionization of atoms by intense laser pulses

We analyzed the two-dimensional (2D) electron momentum distributions of high-energy photoelectrons of atoms in an intense laser field using the second-order strong field approximation (SFA2). The SFA2 accounts for the rescattering of the returning electron with the target ion to first order and its validity is established by comparing with results obtained by solving the time-dependent Schr\"{o}dinger equation (TDSE) for short pulses. By analyzing the SFA2 theory, we confirmed that the yield along the back rescattered ridge (BRR) in the 2D momentum spectra can be interpreted as due to the elastic scattering in the backward directions by the returning electron wave packet. The characteristics of the extracted electron wave packets for different laser parameters are analyzed, including their dependence on the laser intensity and pulse duration. For long pulses we also studied the wave packets from the first and the later returns.Comment: 12 pages, 10 figure

Robust Biosensors for Healthcare Applications: from High-Content Screening to Point-of-Care Testing

Efficient detection of proteins, mammalian cells, microorganisms and other biological systems in complex mixture is essential in disease diagnosis and environmental health. Therefore, technological platforms that provide sensors of high sensitivity, selectivity and stability are greatly desired. Recently, the ‘chemical-nose’ sensing approach has proved to be an effective strategy for profiling bio-relevant targets in complex mixtures. Detecting analytes in complex mixture is a challenge that conventional specificity-based sensors are still trying to solve due to the requirement of prior knowledge of the analyte, which is unknown in many cases. This thesis focuses on how to develop simple and robust chemical-nose sensors for complex mixtures using supramolecular interactions between nanoparticles, fluorescent proteins, enzymes, and fluorescent polymers. We have successfully developed effective sensors for many healthcare applications including chemotherapeutic drug profiling, cancer diagnostics, environmental toxicity and bacterial detection. Throughout this dissertation, there is an emphasis on moving from high-content screening to point-of-care testing, especially in cancer diagnostics. Overall, the chemical-nose sensors provide a simple generic tool for bio-relevant analyte profiling, avoiding additional processing steps prior to screening as seen in traditional methods. More importantly, chemical-nose sensors hold great promise for addressing the needs in personalized screening of disease states and environmental toxicology