Adaptive load balancing algorithm for multiple homing mobile nodes

In places where mobile users can access multiple wireless networks simultaneously, a multipath scheduling algorithm can benefit the performance of wireless networks and improve the experience of mobile users. However, existing literature shows that it may not be the case, especially for TCP flows. According to early investigations, there are mainly two reasons that result in bad performance of TCP flows in wireless networks. One is the occurrence of out-of-order packets due to different delays in multiple paths. The other is the packet loss which is resulted from the limited bandwidth of wireless networks. To better exploit multipath scheduling for TCP flows, this paper presents a new scheduling algorithm named Adaptive Load Balancing Algorithm (ALBAM) to split traffic across multiple wireless links within the ISP infrastructure. Targeting at solving the two adverse impacts on TCP flows, ALBAM develops two techniques. Firstly, ALBAM takes advantage of the bursty nature of TCP flows and performs scheduling at the flowlet granularity where the packet interval is large enough to compensate for the different path delays. Secondly, ALBAM develops a Packet Number Estimation Algorithm (PNEA) to predict the buffer usage in each path. With PNEA, ALBAM can prevent buffer overflow and schedule the TCP flow to a less congested path before it suffers packet loss. Simulations show that ALBAM can provide better performance to TCP connections than its other counterparts.     

Multiple melting in blends of poly(vinylidene fluoride) with isotactic poly(ethyl methacrylate)

Multiple melting phenomena have been studied in blends of poly(vinylidene fluoride) (PVF₂) with low molar mass isotactic poly(ethyl methacrylate) (it-PEMA). In all blends, as well as in pure PVF₂, a transition (T₁) was observed prior to the main melting point (T₂). T₁ is probably connected with the melting of secondarily-crystallized material. In addition to this, a high temperature melting endotherm (T₃) was observed, which could be ascribed completely to recrystallization of PVF₂. The highest transition (T₄) was caused by melting of the σ form of PVF₂. From Hoffman-Weeks plots—T₂ vs. crystallization temperature, T_c — it could be concluded that no thermody amic depression of the melting point of PVF₂ occurred in the blends. The stabilities of PVF₂ crystallites in the various blends were derived from the slopes of Hoffman-Weeks plots and were in good agreement with lamellar thicknesses found from SAXS measurements.     

An analysis method for atomistic abrasion simulations featuring rough surfaces and multiple abrasive particles

We present a molecular dynamics (MD) model system to quantitatively study nanoscopic wear of rough surfaces under two-body and three-body contact conditions with multiple abrasive particles. We describe how to generate a surface with a pseudo-random Gaussian topography which is periodically replicable, and we discuss the constraints on the abrasive particles that lead to certain wear conditions. We propose a post-processing scheme which, based on advection velocity, dynamically identifies the atoms in the simulation as either part of a wear particle, the substrate, or the sheared zone in-between. This scheme is then justified from a crystallographic order point of view. We apply a distance-based contact zone identification scheme and outline a clustering algorithm which can associate each contact atom with the abrasive particle causing the respective contact zone. Finally, we show how the knowledge of each atom’s zone affiliation and a time-resolved evaluation of the substrate topography leads to a break-down of the asperity volume reduction into its components: the pit fill-up volume, the individual wear particles, the shear zone, and the sub-surface substrate compression. As an example, we analyze the time and pressure dependence of the wear volume contributions for two-body and three-body wear processes of a rough iron surface with rigid spherical and cubic abrasive particles.     

Ensemble of multiple instance classifiers for image re-ranking

Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ranking performance. In this paper, we look into this problem of image re-ranking and propose a system that automatically constructs multiple candidate “multi-instance bags (MI-bags)”, which are likely to contain relevant images. These automatically constructed bags are then utilized by ensembles of Multiple Instance Learning (MIL) classifiers and the images are re-ranked according to the final classification responses. Our method is unsupervised in the sense that, the only input to the system is the text query itself, without any user feedback or annotation. The experimental results demonstrate that constructing multiple instance bags based on the retrieval order and utilizing ensembles of MIL classifiers greatly enhance the retrieval performance, achieving on par or better results compared to the state-of-the-art.     

Fall detection for multiple pedestrians using depth image processing technique

A fall detection method based on depth image analysis is proposed in this paper. As different from the conventional methods, if the pedestrians are partially overlapped or partially occluded, the proposed method is still able to detect fall events and has the following advantages: (1) single or multiple pedestrian detection; (2) recognition of human and non-human objects; (3) compensation for illumination, which is applicable in scenarios using indoor light sources of different colors; (4) using the central line of a human silhouette to obtain the pedestrian tilt angle; and (5) avoiding misrecognition of a squat or stoop as a fall. According to the experimental results, the precision of the proposed fall detection method is 94.31% and the recall is 85.57%. The proposed method is verified to be robust and specifically suitable for applying in family homes, corridors and other public places.     

Inheritance Of Resistance To Mammalian Herbivores and Of Plant Defensive Chemistry In A <Emphasis Type="Italic">Eucalyptus</Emphasis> Species

Hybridization in plants provides an opportunity to investigate the patterns of inheritance of hybrid resistance to herbivores, and of the plant mechanisms conferring this resistance such as plant secondary metabolites. We investigated how inter-race differences in resistance of Eucalyptus globulus to a generalist mammalian herbivore, Trichosurus vulpecula, are inherited in their F₁ hybrids. We assessed browsing damage of three-year-old trees in a common environment field trial on four hybrid types of known progeny. The progency were artificial intra-race crosses and reciprocal inter-race F₁ hybrids of two geographically distinct populations (races) of E. globulus; north-eastern Tasmania and south-eastern Tasmania. Populations of trees from north-eastern Tasmania are relatively susceptible to browsing by T. vulpecula, while populations from south-eastern Tasmania are more resistant. We assessed the preferences of these trees in a series of paired feeding trials with captive animals to test the field trial results and also investigated the patterns of inheritance of plant secondary metabolites. Our results demonstrated that the phenotypic expression of resistance of the inter-race F₁ hybrids supported the additive pattern of inheritance, as these hybrids were intermediate in resistance compared to the pure parental hybrids. The expression of plant secondary metabolites in the F₁ hybrids varied among groups of individual compounds. The most common pattern supported was dominance towards one of the parental types. Together, condensed tannins and essential oils appeared to explain the observed patterns of resistance among the four hybrid types. While both chemical groups were inherited in a dominant manner in the inter-race F₁ hybrids, the direction of dominance was opposite. Their combined concentration, however, was inherited in an additive manner, consistent with the phenotypic differences in browsing.     

CFD modeling of pilot-scale pump-mixer: Single-phase head and power characteristics

The present work involves single-phase computational fluid dynamics (CFD) simulations of continuous flow pump-mixer employing top-shrouded Rushton turbines with trapezoidal blades. Baffle—impeller interaction has been modeled using sliding mesh and multiple reference frame approaches. Standard k-ε model has been used for turbulence modeling. Several CFD runs representing different combinations of geometric and process parameters have been carried out. Results of CFD simulations have been used to find out two macroscopic performance parameters of pump-mixer—power consumption and head generated by the impeller. The simulation results have been compared with the experimental data obtained on a pilot-scale setup. Good agreement between CFD predictions and experimental results is observed. In most cases, sliding mesh approach is found to perform better than multiple reference frame approach. Details from CFD simulations have been used to have an insight into the pumping action of the impeller.     

Experimental design approach to calcium carbonate precipitation in a semicontinuous process

The production of precipitated calcium carbonate, PCC, by a semicontinuous process of slaked lime carbonation was performed in a bench-scale chemical reactor, fully controlled by means of custom built electronics and software for the personal computer. Calcite crystals, with different characteristic morphologies (rhombohedral, truncated prismatic, scalenohedral, spheroidal or chain-like agglomerates) were produced by varying a range of process parameters, like temperature, supersaturation, gas mixture flow rate, stirring rate and mass concentration of Ca(OH)₂ suspension. In order to identify the effects of the chosen process parameters on the PCC morphology and on the related specific surface area, as well as on the extent of CO₂ conversion, an empirical approach based on the experimental design techniques was employed. A multiple correlation analysis of the obtained data suggests that temperature and conductivity significantly influence the PCC morphology, while CO₂ conversion is principally influenced by stirring rate, conductivity and gas mixture flow rate.     

高速高机动目标自适应IMM跟踪算法

针对目标运动过程中有转弯机动等复杂运动模式的高速高机动目标,设计了自适应两层IMM跟踪算法。该算法内层由改进的机动目标当前统计模型构成,把目标速度方向角作为伪测量值进行滤波,实时获得目标的角速度和角加速度;外层模型由常速模型和曲线模型构成,把内层模型得到的切向加速度和转弯角速度作为曲线模型参数,利用IMM算法进行滤波。仿真结果表明,该算法对高速高机动目标具有较高的跟踪精度,算法实现简单,具有一定的实际应用价值。     

FAST LINEAR ESTIMATION METHODS FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. Three linear methods for estimating parameter values of vector auto-regressive moving-average (VARMA) models which are in general at least an order of magnitude faster than maximum likelihood estimation are developed in this paper. Simulation results for different model structures with varying numbers of component series and observations suggest that the accuracy of these procedures is in most cases comparable with maximum likelihood estimation. Procedures for estimating parameter standard error are also discussed and used for identification of nonzero elements in the VARMA polynomial structures. These methods can also be used to establish the order of the VARMA structure. We note, however, that the primary purpose of these estimates is to generate initial estimates for the nonzero parameters in order to reduce subsequent computational time of more efficient estimation procedures such as exact maximum likelihood.