Emerging Applications for Cyber Transportation Systems

Recent advances in connected vehicles and autonomous driving are going to change the face of ground trans- portation as we know it. This paper describes the design and evaluation of several emerging applications for such a cyber transportation system （CTS）. These applications have been designed using holistic approaches, which consider the unique roles played by the human drivers, the transportation system, and the communication network. They can improve driver safety and provide on-road infotainment. They can also improve transportation operations and efficiency, thereby benefiting travelers and attracting investment from both government agencies and private businesses to deploy infrastructures and bootstrap the evolutionary process of CTS.     

Primal explicit max margin feature selection for nonlinear support vector machines

Embedding feature selection in nonlinear support vector machines (SVMs) leads to a challenging non-convex minimization problem, which can be prone to suboptimal solutions. This paper develops an effective algorithm to directly solve the embedded feature selection primal problem. We use a trust-region method, which is better suited for non-convex optimization compared to line-search methods, and guarantees convergence to a minimizer. We devise an alternating optimization approach to tackle the problem efficiently, breaking it down into a convex subproblem, corresponding to standard SVM optimization, and a non-convex subproblem for feature selection. Importantly, we show that a straightforward alternating optimization approach can be susceptible to saddle point solutions. We propose a novel technique, which shares an explicit margin variable to overcome saddle point convergence and improve solution quality. Experiment results show our method outperforms the state-of-the-art embedded SVM feature selection method, as well as other leading filter and wrapper approaches.     

RAFT: A router architecture with frequency tuning for on-chip networks

With increasing number of cores being integrated on a single die, Network-on-Chips (NoCs) have become the de-facto standard in providing scalable communication backbones for these multi-core chips. NoCs have a significant impact on the system’s performance, power and reliability. However, NoCs can be plagued by higher power consumption and degraded throughput if the network and router are not designed properly. Towards this end, this paper proposes a novel router architecture, where we tune the frequency of a router in response to network load to manage both performance and power. We propose three dynamic frequency tuning techniques, FreqBoost, FreqThrtl and FreqTune, targeted at congestion and power management in NoCs. We also propose and evaluate a novel fine-grained frequency tuning scheme where we vary the number of virtual-channels in a router dynamically. As a further optimization to these schemes, we propose a frequency tuning scheme where we tune the frequency of the four ports of a mesh router separately from the local port. As enablers for these techniques, we exploit Dynamic Voltage and Frequency Scaling (DVFS) and the imbalance in a generic router pipeline through time stealing. We also evaluate and analyze the proposed schemes from the point of view of reliability against soft error vulnerability and provide guidelines in choosing the appropriate scheme when reliability is the prime design constraint.Experiments using synthetic workloads on an 8 × 8 wormhole-switched mesh interconnect show that FreqBoost is a better choice for reducing average latency (maximum 40%) while, FreqThrtl provides the maximum benefits in terms of power saving and energy delay product (EDP). The FreqTune scheme is a better candidate for optimizing both performance and power, achieving on an average 36% reduction in latency, 13% savings in power (up to 24% at high load), and 40% savings (up to 70% at high load) in EDP. With application benchmarks, we observe IPC improvement up to 23% using our design. Our analysis shows FreqBoost to be the most robust scheme amongst the three schemes when reliability is a concern.     

Beam search algorithms for multilabel learning

Multilabel learning is a machine learning task that is important for applications, but challenging. A recent method for multilabel learning called probabilistic classifier chains (PCCs) has several appealing properties. However, PCCs suffer from the computational issue that inference (i.e., predicting the label of an example) requires time exponential in the number of tags. Also, PCC accuracy is sensitive to the ordering of the tags while training. In this paper, we show how to use the classical technique of beam search to solve both these problems. Specifically, we show how to apply beam search to make inference tractable, and how to integrate beam search with training to determine a suitable tag ordering. Experimental results on a range of datasets show that the proposed improvements yield a state-of-the-art method for multilabel learning.     

Personalized ranking in web databases: establishing and utilizing an appropriate workload

The emergence of the deep Web has given a new connotation to the concept of ranking database query results. Earlier approaches for ranking either resorted to analyzing frequencies of database values and query logs or establishing user profiles. In contrast, an integrated approach, based on the notion of a similarity model, for holistically supporting user- and query-dependent ranking has been recently proposed (Telang et al. in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2011). An important component of this framework is a workload consisting of ranking functions, wherein each function represents an individual user’s preferences towards the results of a specific query. At the time of answering a query for which no prior ranking function exists, the similarity model is employed, and is expected to ensure a good quality of ranking as long as a ranking function for a very similar user-query pair exists in this workload. In this paper, we address the problem of determining an appropriate set of user-query pairs to form a workload of ranking functions to support user- and query-dependent ranking for Web databases. We propose a novel metric, termed workload goodness, that quantifies the notion of a “good” workload into an absolute value. The process of finding such a workload of optimal goodness is a combinatorially explosive problem; therefore, we propose a heuristic solution, and advance three approaches for determining an acceptable workload, in a static as well as a dynamic environment. We discuss the effectiveness of our proposal analytically as well as experimentally over two Web databases.     

Modeling of Convective Drying of Sawdust Using a Reaction Engineering Approach

Drying as a simultaneous heat and mass transfer process can be modeled via the reaction engineering approach (REA) where the apparent activation energy of the material is established and related to its moisture content during drying. This relationship is unique as the normalized activation energies can be collapsed into a single equation irrespective of the drying conditions and dryer types. Here, REA was applied to model the drying kinetics of sawdust using convective hot air in a laboratory setup. The normalized (relative) activation energy curve generated from one drying experiment was employed to predict the drying kinetics and temperature profiles. The REA can describe well the convective drying kinetics of sawdust, and major physics of the drying process was captured well with this technique.     

Microwave drying at various conditions modeled using the reaction engineering approach

ABSTRACT

One of the most significant process intensification schemes in drying is microwave drying. Modeling the process of microwave drying is very useful. The lumped reaction engineering approach (REA) is now coupled with appropriate equations for modeling microwave heating. Here, a slight modification of the equilibrium activation energy is needed since the product temperature is higher than the ambient temperature. Unlike the diffusion-based approach, the REA drying parameters were generated from minimum number of drying runs. It has been found that the modifications lead to excellent agreements between the predicted and experimental data. The results of modeling match well with the experimental data. The overall model is accurate to describe the moisture content and temperature profiles. Comparisons with the diffusion-based approach indicate that the REA can achieve comparable or even better agreement toward the experimental data. This exercise has demonstrated that a simple combination of the lumped reaction engineering approach and the microwave energy absorption is versatile in predicting the microwave drying process accurately; thus, this worked example will be illustrative for future needed studies.     

Electrospinning of Polymers,Their Modeling and Applications

Although there are several methods for obtaining sub-micro or nanofibers, electrospinning is perhaps the most versatile process. Nanotechnology has been widely accepted as dealing with the science and technology where at least one dimension is of roughly 1 to 100 nm. Electrospinning has been recognized as a feasible technique for the fabrication of polymeric nanofiber yarns. Various materials including polymers, composites, ceramics and metals have been electrospun into nanofibers. The nanofibers thus produced exhibit novel and significantly improved physical, chemical and biological properties due to their nanolevel size. In this article, the electrospinning process, along with its modeling equations and applications have been discussed. Some typical case studies regarding electrospinning under various categories have also been discussed.     

Phonon dispersion in binary metallic glasses

The theoretical computation of the phonon dynamics of two binary metallic glasses Zr₆₇Ni₃₃ and Co₆₇Zr₃₃ have been studied from the model potential formalism using three different theoretical models proposed by Hubbard-Beeby, Takeno-Goda, and Bhatia-Singh. Five local field correction functions, viz., Hartree, Taylor, Ichimaru-Utsumi, Farid et al., and Sarkar et al., are used for the first time in the present investigation to study the screening influence on the aforesaid properties. The pair potential is computed in the Wills-Harrison form and used to study the eigenfrequencies of longitudinal and transverse phonon modes. The present data on phonon dispersion curves of the Zr₆₇Ni₃₃ glass are compared with the available MD results at different temperatures. To explain the electron-ion interaction, the pseudo-alloy-atom model is applied for the first time instead of the Vegard’s law. Further, thermodynamic and elastic properties have also been calculated from the elastic part of the phonon dispersion curves. Computed yielding of the Zr₆₇Ni₃₃ glass is found in fair agreement with the available data.