Is Topology-Transparent Scheduling Really Inefficient in Static Multihop Networks?

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Performance Improvement of Topology-Transparent Broadcast Scheduling in Mobile Ad Hoc Networks

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Topology-Transparent Broadcast Scheduling with Erasure Coding in Wireless Networks

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Topology-Transparent Scheduling in Mobile Ad Hoc Networks With Multiple Packet Reception Capability

Recent advances in the physical layer have enabled wireless devices to have multiple packet reception (MPR) capability, which is the capability of decoding more than one packet, simultaneously, when concurrent transmissions occur. In this paper, we focus on the interaction between the MPR physical layer and the medium access control (MAC) layer. Some random access MAC protocols have been proposed to improve the network performance by exploiting the powerful MPR capability. However, there are very few investigations on the schedule-based MAC protocols. We propose a novel m-MPR-l-code topology-transparent scheduling ((m, l)-TTS) algorithm for mobile ad hoc networks with MPR, where m indicates the maximum number of concurrent transmissions being decoded, and l is the number of codes assigned to each user. Our algorithm can take full advantage of the MPR capability to improve the network performance. The minimum guaranteed throughput and average throughput of our algorithm are studied analytically. The improvement of our (m, l)-TTS algorithm over the conventional topology-transparent scheduling algorithms with the collision-based reception model is linear with m. The simulation results show that our proposed algorithm performs better than slotted ALOHA as well.published_or_final_versio

Topology-transparent distributed multicast and broadcast scheduling in mobile ad hoc networks

Transmission scheduling is a key problem in mobile ad hoc networks. Many transmission scheduling algorithms have been proposed to maximize the spatial reuse and minimize the time-division multiple-access (TDMA) frame length in mobile ad hoc networks. Most algorithms are dependent on the exact network topology and cannot adapt to the dynamic topology in a mobile wireless network. To overcome this limitation, several topology-transparent scheduling algorithms have been proposed. The slots are assigned to guarantee that there is at least one collision-free time slot in each frame. In this paper, we consider multicast and broadcast, and propose a novel topology-transparent distributed scheduling algorithm. Instead of guaranteeing at least one collision-free transmission, the proposed algorithm guarantees one successful transmission exceeding a given probability, and achieves a much better average throughput. The simulation results show that the performance of our proposed algorithm is much better than the conventional TDMA and other existing algorithms in most cases. © 2012 IEEE.published_or_final_versio

Self-assembled GaInNAs/GaAsN quantum dot lasers: solid source molecular beam epitaxy growth and high-temperature operation

Self-assembled GaInNAs quantum dots (QDs) were grown on GaAs (001) substrate using solid-source molecular-beam epitaxy (SSMBE) equipped with a radio-frequency nitrogen plasma source. The GaInNAs QD growth characteristics were extensively investigated using atomic-force microscopy (AFM), photoluminescence (PL), and transmission electron microscopy (TEM) measurements. Self-assembled GaInNAs/GaAsN single layer QD lasers grown using SSMBE have been fabricated and characterized. The laser worked under continuous wave (CW) operation at room temperature (RT) with emission wavelength of 1175.86 nm. Temperature-dependent measurements have been carried out on the GaInNAs QD lasers. The lowest obtained threshold current density in this work is ∼1.05 kA/cm2from a GaInNAs QD laser (50 × 1,700 µm2) at 10 °C. High-temperature operation up to 65 °C was demonstrated from an unbonded GaInNAs QD laser (50 × 1,060 µm2), with high characteristic temperature of 79.4 K in the temperature range of 10–60 °C

Topology-Transparent Scheduling in Mobile Ad Hoc Networks Supporting Heterogeneous Quality of Service Guarantees

Transmission scheduling plays a critical role in mobile ad hoc networks. Many transmission scheduling algorithms have been proposed to maximize the spatial reuse and minimize the time-division multiple-access (TDMA) frame length. Most algorithms require information on the network topology and cannot adapt to the dynamic topology in mobile scenarios. To overcome this limitation, topology-transparent scheduling algorithms have been proposed. Most of them, based on Galois field theory, Latin square, and block design theory, assign time slots to users and guarantee that there is at least one collision-free slot in each frame for each user. To the best of our knowledge, none of these topology-transparent algorithms support multiple quality of service (QoS) requirements. In this paper, we exploit the variable-weight optical orthogonal codes (VW-OOC) to design a topology-transparent scheduling algorithm in wireless ad hoc networks with multiple QoS levels. We study the performance, in terms of minimum guaranteed throughput and average throughput, of our proposed algorithm analytically and by extensive simulations.published_or_final_versio

NR-2L: A Two-Level Predictor for Identifying Nuclear Receptor Subfamilies Based on Sequence-Derived Features

Nuclear receptors (NRs) are one of the most abundant classes of transcriptional regulators in animals. They regulate diverse functions, such as homeostasis, reproduction, development and metabolism. Therefore, NRs are a very important target for drug development. Nuclear receptors form a superfamily of phylogenetically related proteins and have been subdivided into different subfamilies due to their domain diversity. In this study, a two-level predictor, called NR-2L, was developed that can be used to identify a query protein as a nuclear receptor or not based on its sequence information alone; if it is, the prediction will be automatically continued to further identify it among the following seven subfamilies: (1) thyroid hormone like (NR1), (2) HNF4-like (NR2), (3) estrogen like, (4) nerve growth factor IB-like (NR4), (5) fushi tarazu-F1 like (NR5), (6) germ cell nuclear factor like (NR6), and (7) knirps like (NR0). The identification was made by the Fuzzy K nearest neighbor (FK-NN) classifier based on the pseudo amino acid composition formed by incorporating various physicochemical and statistical features derived from the protein sequences, such as amino acid composition, dipeptide composition, complexity factor, and low-frequency Fourier spectrum components. As a demonstration, it was shown through some benchmark datasets derived from the NucleaRDB and UniProt with low redundancy that the overall success rates achieved by the jackknife test were about 93% and 89% in the first and second level, respectively. The high success rates indicate that the novel two-level predictor can be a useful vehicle for identifying NRs and their subfamilies. As a user-friendly web server, NR-2L is freely accessible at either http://icpr.jci.edu.cn/bioinfo/NR2L or http://www.jci-bioinfo.cn/NR2L. Each job submitted to NR-2L can contain up to 500 query protein sequences and be finished in less than 2 minutes. The less the number of query proteins is, the shorter the time will usually be. All the program codes for NR-2L are available for non-commercial purpose upon request

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches