Randomized scheduling algorithms for high-aggregate bandwidth switches

The aggregate bandwidth of a switch is its port count multiplied by its operating line rate. We consider switches with high-aggregate bandwidths; for example, a 30-port switch operating at 40 Gb/s or a 1000-port switch operating at 1 Gb/s. Designing high-performance schedulers for such switches with input queues is a challenging problem for the following reasons: (1) high performance requires finding good matchings; (2) good matchings take time to find; and (3) in high-aggregate bandwidth switches there is either too little time (due to high line rates) or there is too much work to do (due to a high port count). We exploit the following features of the switching problem to devise simple-to-implement, high-performance schedulers for high-aggregate bandwidth switches: (1) the state of the switch (carried in the lengths of its queues) changes slowly with time, implying that heavy matchings will likely stay heavy over a period of time and (2) observing arriving packets will convey useful information about the state of the switch. The above features are exploited using hardware parallelism and randomization to yield three scheduling algorithms - APSARA, LAURA, and SERENA. These algorithms are shown to achieve 100% throughput and simulations show that their delay performance is quite close to that of the maximum weight matching, even when the traffic is correlated. We also consider the stability property of these algorithms under generic admissible traffic using the fluid-model technique. The main contribution of this paper is a suite of simple to implement, high-performance scheduling algorithms for input-queued switches. We exploit a novel operation, called MERGE, which combines the edges of two matchings to produce a heavier match, and study of the properties of this operation via simulations and theory. The stability proof of the randomized algorithms we present involves a derandomization procedure and uses methods which may have wider applicability.     

Novel Approach of Key Predistribution for Grid Based Sensor Networks

Wireless Personal Communications - Grid based sensor networks are significant for applications such as monitoring goods in a warehouse, studying traffic level of city streets, monitoring energy...     

Queue length‐based load balancing in data center networks

Traffic load balancing in data centers is an important requirement. Traffic dynamics and possibilities of changes in the topology (e.g., failures and asymmetries) make load balancing a challenging task. Existing end‐host–based schemes either employ the predominantly used ECN or combine it with RTT to get congestion information of paths. Both congestion signals, ECN and RTT, have limitations; ECN only tells whether the queue length is above or below a threshold value but does not inform about the extent of congestion; similarly, RTT in data center networks is on the scale of up to few hundreds of microseconds, and current data center operating systems lack fine‐grained microsecond‐level timers. Therefore, there is a need of a new congestion signal which should give accurate information of congestion along the path. Furthermore, in end‐host–based schemes, detecting asymmetries in the topology is challenging due to the inability to accurately measure RTT on the scale of microseconds. This paper presents QLLB, an end‐host–based, queue length–based load balancing scheme. QLLB employs a new queue length–based congestion signal that gives an exact measure of congestion along the paths. Furthermore, QLLB uses relative‐RTT to detect asymmetries in the topology. QLLB is implemented in ns‐3 and compared with ECMP, CONGA, and Hermes. The results show that QLLB significantly improves performance of short flows over the other schemes and performs within acceptable level, of CONGA and Hermes, for long flows. In addition, QLLB effectively detects asymmetric paths and performs better than Hermes under high loads.     

Epitaxial lift-off GaAs HEMT's

An extensive study of epitaxial lift-off (ELO) Al_0.3Ga _0.7As/GaAs modulation doped heterostructure high electron mobility field-effect transistors (HEMT's) is presented. Effects of ELO on electron transport properties of two-dimensional electron gas at AlGaAs/GaAs interface are investigated. An ELO HEMT with 1.5 μm gate length had a maximum extrinsic transconductance g_m-max=125 mS/mm, a unity current gain cut-off frequency f_t=10.5 GHz, and a maximum frequency of oscillation f_max=12 GHz. Statistical distributions of maximum intrinsic transconductance of ELO HEMT's are presented and compared with their on-wafer counterparts. Stability of the ELO HEMT's has also been evaluated by continuous operation at room temperature under dc bias     

An application of univariate marginal distribution algorithm in MIMO communication systems

The paper discusses a sequence detector based on univariate marginal distribution algorithm (UMDA) that jointly estimates the symbols transmitted in a multiple input multiple output (MIMO) communication system. While an optimal maximum likelihood detection using an exhaustive search method is prohibitively complex, it has been shown that sphere decoder (SD) achieves the optimal bit error rate (BER) performance with polynomial time complexity for smaller array sizes. However, the worst‐case complexity of SD is exponential in the problem dimensions, this brings in question its practical implementation for larger number of spatial layers and for higher‐order signal constellation. The proposed detector shows promising results for this overly difficult and complicated operating environment, confirmed through simulation results. A performance comparison of the UMDA detector with SD is presented for higher‐order complex MIMO architectures with limited average transmit power. The proposed detector achieves substantial performance gain for higher‐order systems attaining a near optimal BER performance with reduced computational complexity as compared with SD. Copyright © 2009 John Wiley & Sons, Ltd.     

Characterizing cerebral oxygen metabolism employing oxygen-17 MRI/MRS at high fields

This article provides a comprehensive overview of oxygen (¹⁷O) magnetic resonance spectroscopy and imaging, including the advantages and challenges offered by the different methods developed thus far. The physiological role and relevance of oxygen, and its participation in aerobic metabolism, are addressed to emphasize the importance of the investigations and the efforts related to these developments. Furthermore, a number of methods employed in the determination of the cerebral metabolic rate of oxygen in neural cells will be presented, focusing primarily on methodologies enabling absolute quantification.     

Accelerated Design of Eutectic High Entropy Alloys by ICME Approach

Eutectic high entropy alloy with seven components is designed based on the integrated computational materials engineering (ICME) framework. The framework includes thermodynamic prediction using calculation of phase diagrams (CALPHAD), microstructure simulation using phase-field method, and experimental validation. The designed alloy shows the eutectic structure consisting of FCC and laves phase in the composition range from 8.25 to 10 at. pct Ta. The simulation and experimental results are co-related and a framework is proposed that can be used for high entropy alloy design subjected to various manufacturing processes.

     

Synthesis of nanostructured copper hexacyanidoferrate and its application in voltammeteric detection of salbutamol

Nanostructured copper hexacyanidoferrate has been synthesized and characterized using elemental analysis, atomic absorption spectroscopy, thermal and infrared spectral studies. The transmission electron microscopic studies of the synthesized material showed that it consisted of irregular oval and rod shaped particles with a size range 70–100 nm. Nanostructured copper hexacyanidoferrate modified glassy carbon electrode was characterized by cyclic voltammetery and nanostructured copper hexacyanidoferrate–carbon nanotube composite material modified glassy carbon electrode has been used for electrocatalytic oxidation of salbutamol. The electrode modified with composite material was found to reduce the peak potential of oxidation of salbutamol by nearly 90 mV.     

Estimation of the Kubelka—Munk scattering coefficient from single particle scattering parameters

Computer colour matching of paints is based on the determination of the Kubelka—Munk absorption (K) and scattering coefficients (S) of pigments. K and S are sensitive to particle size in the range of sizes employed in paint technology. The K—M theory does not provide any guidelines for the correction of the values of K and S with a change in particle size. On the other hand the Mie theory linear scattering coefficient can be calculated from a knowledge of size and refractive index of the pigment. A number of relations correlating the K—M scattering coefficient and the linear scattering coefficient have been proposed in the literature. Attempts are made to estimate the K—M scattering coefficient using a relation suggested by Mudgett and Richards and to make a comparison with experimental values. The Mie theory equations being complex, simplified equations applicable to pigment sizes for the calculation of Mie theory parameters are proposed. The viability of the simplified equations has been established by comparing the results with those obtained using the full Mie equations.The K—M scattering coefficient for titanium dioxide pigments has been determined from reflectance measurements. The particle size of titanium dioxide has been determined by a light scattering method. The K—M scattering coefficient calculated from the linear scattering coefficient obtained using the simplified expressions agrees with experimental results. This suggests a method for the correction of the values of K and S with changes in pigment particle size.     

Nanomedicine-Based Delivery Strategies for Breast Cancer Treatment and Management

Breast cancer is one of the most common types of cancer among women globally. It is caused by mutations in the estrogen/progesterone receptors and conventional treatment methods are commonly utilized. About 70–80 percent of individuals with the early-stage non-metastatic disease may be cured. Conventional treatment is far less than the optimal ratio, as demonstrated through the high mortality rate of women with this cancer. However, conventional treatment methods like surgery, radiotherapy, and chemotherapy are not as effective as expected and lead to concerns about low bioavailability, low cellular uptake, emerging resistance, and adverse toxicities. A nanomedicine-based approach is a promising alternative for breast cancer treatment. The present era is witnessing rapid advancements in nanomedicine as a platform for investigating novel therapeutic applications and modern intelligent healthcare management strategies. This paper focuses on nanomedicine-based therapeutic interventions that are becoming more widely accepted for improving treatment effectiveness and reducing undesired side effects in breast cancer patients. By evaluating the state-of-the-art tools and taking the challenges involved into consideration, various aspects of the proposed nano-enabled therapeutic approaches have been discussed in this review.