Novel design to improve band to band tunneling and gate induced drain leakages (GIDL) in cylindrical gate all around (GAA) MOSFET

Microsystem Technologies - In this paper a cylindrical Dual Metal (DM) Dielectric Engineered (DE) Gate All Around (GAA) MOSFET has been proposed to resolve a big issue of Gate Inducted Drain...     

TESLA: an extended study of an energy-saving agent that leverages schedule flexibility

This paper presents transformative energy-saving schedule-leveraging agent (TESLA), an agent for optimizing energy usage in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. This paper provides four key contributions: (i) online scheduling algorithms, which are at the heart of TESLA, to solve a stochastic mixed integer linear program for energy-efficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; (iii) an extensive analysis on energy savings achieved by TESLA; and (iv) surveys of real users which indicate that TESLA’s assumptions of user flexibility hold in practice. TESLA was evaluated on data gathered from over 110,000 meetings held at nine campus buildings during an 8-month period in 2011–2012 at the University of Southern California and Singapore Management University. These results and analysis show that, compared to the current systems, TESLA can substantially reduce overall energy consumption.     

Microneedles for transdermal drug delivery: a systematic review

Transdermal route has been explored for various agents due to its advantage of bypassing the first pass effect and sustained release of drug. Due to strong barrier properties of the skin, mainly stratum corneum (SC), the delivery of many therapeutic agents across the skin has become challenging. Few drugs with specific physicochemical properties (molecular weight <500?Da, adequate lipophilicity, and low melting point) can be effectively administered via transdermal route. However, delivery of hydrophilic drugs and macromolecular agents including peptides, DNA and small interfering RNA is challenging. Drug penetration through the SC may involve bypass or reversible disruption of SC layer by various means. Recently, the use of micron-scale needles has been proposed in increasing skin permeability and shown to dramatically increase permeation, especially for macromolecules. Microneedles (MNs) can penetrate through the SC layer of the skin into the viable epidermis, avoiding contact with nerve fibers and blood vessels that reside primarily in the dermal layer. This review summarizes the types of MNs and fabrication techniques of different types of MNs. The safety aspects of the materials used for fabrication have been discussed in detail. Biological applications and relevant phase III clinical trials are also highlighted.     

DFuzzy: a deep learning-based fuzzy clustering model for large graphs

Graph clustering is successfully applied in various applications for finding similar patterns. Recently, deep learning- based autoencoder has been used efficiently for detecting disjoint clusters. However, in real-world graphs, vertices may belong to multiple clusters. Thus, it is obligatory to analyze the membership of vertices toward clusters. Furthermore, existing approaches are centralized and are inefficient in handling large graphs. In this paper, a deep learning-based model ‘DFuzzy’ is proposed for finding fuzzy clusters from large graphs in distributed environment. It performs clustering in three phases. In first phase, pre-training is performed by initializing the candidate cluster centers. Then, fine tuning is performed to learn the latent representations by mining the local information and capturing the structure using PageRank. Further, modularity is used to redefine clusters. In last phase, reconstruction error is minimized and final cluster centers are updated. Experiments are performed over real-life graph data, and the performance of DFuzzy is compared with four state-of-the-art clustering algorithms. Results show that DFuzzy scales up linearly to handle large graphs and produces better quality of clusters when compared to state-of-the-art clustering algorithms. It is also observed that deep structures can help in getting better graph representations and provide improved clustering performance.     

The propagation of pressure in a gelled waxy oil pipeline as studied by particle imaging velocimetry

Paraffinic crude oils in pipelines may form waxy gels during flow shutdowns. These gels can be dislodged by applying pressure if the wall shear stress, proportional to the local pressure gradient, exceeds the gel yield stress. The simplest models assume that the axial pressure profile becomes linear immediately after a jump in upstream pressure, but this fails to account for gel time‐dependent rheology or the effect of gel voids on pressure wave propagation. To investigate the former factor, pressure profile and particle imaging velocimetry (PIV) measurements were performed on a model oil gelled under pressure to reduce void formation. After a jump in upstream pressure to a value insufficient to restart flow, the axial pressure profile becomes linear in a two‐step process, with an immediate small rise in downstream pressure followed by a time‐delayed jump. The local downstream gel deformation measured by PIV exhibits similar two‐step time dependence. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Analysis of fractal inter frame video coding using parallel approach

Digital video has many applications varying from telecommunications to broadcasting and so on. Video compression techniques have evolved over the past two decades with prominent technique being developed using fractal. However, this technique was not so popular because of its computationally intensive nature. This paper proposes an inter frame video compression technique, which consists of a combination of a block matching using fractal compression. The proposed algorithm is implemented on CUDA-enabled GPU which significantly reduces the encoding time of the video and provides a very high compression ratio with reasonable quality of the decoded video. Extensive simulations were carried out to analyze the performance of the proposed algorithm.     

Every team deserves a second chance: an extended study on predicting team performance

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.     

Analytical modeling of bidirectional multi‐channel IEEE 802.11 MAC protocols

This paper presents an analytical approach to model the bi‐directional multi‐channel IEEE 802.11 MAC protocols (Bi‐MCMAC) for ad hoc networks. Extensive simulation work has been done for the performance evaluation of IEEE 802.11 MAC protocols. Since simulation has several limitations, this work is primarily based on the analytical approach. The objective of this paper is to show analytically the performance advantages of Bi‐MCMAC protocol over the classical IEEE 802.11 MAC protocol. The distributed coordination function (DCF) mode of medium access control (MAC) is considered in the modeling. Two different channel scheduling strategies, namely, random channel selection and fastest channel first selection strategy are also presented in the presence of multiple channels with different transmission rates. M/G/1 queue is used to model the protocols, and stochastic reward nets (SRNs) are employed as a modeling technique as it readily captures the synchronization between events in the DCF mode of access. The average system throughput, mean delay, and server utilization of each MAC protocol are evaluated using the SRN formalism. We also validate our analytical model by comparison with simulation results. The results obtained through the analytical modeling approach illustrate the performance advantages of Bi‐MCMAC protocols with the fastest channel first scheduling strategy over the classical IEEE 802.11 protocol for TCP traffic in wireless ad hoc networks. Copyright © 2010 John Wiley & Sons, Ltd.