23471429
Improving TCP Throughput over HSDPA Networks
The various link adaptation techniques employed by High Speed Downlink Packet Access (HSDPA) in the third generation (3G) networks augment the bandwidth oscillation, which is identified as one of the most important factors resulting in the throughput deterioration of Transmission Control Protocol (TCP). In this paper, we firstly explain why the bandwidth oscillation degrades the TCP performance through a special simulation experiment. Subsequently, a split connection Window Adaptation TCP Proxy is proposed to improve the TCP throughput over HSDPA networks. In this solution, the built-in attributes of HSDAP system are sufficiently utilized. In order to effectively use the precious cellular link resources, the length of the queue connected with it is intentionally kept around the reference value through adjusting the sending window size of TCP proxy based on the dynamic values of varying bandwidth. A discrete-time stochastic state space model is formulated to analyze the system stability. The validity of enhanced scheme is verified through simulation experiments. The performance of TCP proxy is compared with the standard TCP protocol. The numerical results show that our TCP proxy is able to keep the cellular link utilization over 90%, and to improve TCP throughput by 100% under most conditions.
26646151
Research on Radar Targets Recognition by Extracting 3-D Characteristic from ISAR Images
Based on ISAR imaging technology and Computer Vision theory, a novel target recognition method extracting the 3-Dimensional characteristic of targets from ISAR images sequence is presented in this paper, which can get higher recognition rate with fewer samples.
15224667
Remaining Delivery Time Estimation Based Routing for Intermittently Connected Mobile Networks
We propose Remaining Delivery Time Estimation based Routing (RDTER), a single-copy routing protocol for intermittently connected mobile networks. Nodes predict the Remaining Delivery Time (RDT) to each destination based on the distance estimations obtained from the recent two consecutive contact records. The distance estimation is set to be proportional to the inter-node encounter age, which is updated with transitivity upon each contact. Packet is forwarded to the encountered node whose RDT estimation to the destination is smaller. RDTER provides better forwarding direction, because a smaller RDT estimation means a node is more likely to meet the destination sooner. Thus the routing performance, especially the average delay could be improved. Simulations show that RDTER performs well under the random waypoint mobility model.
3230690
A new verification-based fast-match for large vocabulary continuous speech recognition
Acoustic fast-match is a popular way to accelerate the search in large-vocabulary continuous-speech recognition, where an efficient method is used to identify poorly scoring phonemes and discard them from detailed evaluation. In this paper we view acoustic fast-match as a verification problem, and hence develop an efficient likelihood ratio test, similar to other verification scenarios, to perform the fast match. Various aspects of the test like the design of alternate hypothesis models and the setting of phoneme look-ahead durations and decision thresholds are studied, resulting in an efficient implementation. The proposed fast-match is tested in a large vocabulary speech recognition task and it is demonstrated that depending on the decision threshold, it leads to 20-30% improvement in speed without any loss in recognition accuracy. In addition, it significantly outperforms a similar test based on using likelihoods only, which fails, in our setting, to bring any improvement in speed-accuracy trade-off. In a larger set of experiments with varying acoustic and task conditions, similar improvements are observed for the fast-match with the same model and setting. This indicates the robustness of the proposed technique. The gains due to the proposed method are obtained within a highly efficient 2-pass search strategy and similar or even higher gains are expected in other alternative search architectures.
42839671
A Novel Integrated Supporting System for Mesh-Pull Based P2P IPTV
This paper presents a novel integrated supporting system for the mesh-pull based P2P IPTV. The proposed system can monitor the service quality of each overlays within the system by initiating random walks to sample key metrics of the system periodically and analyze the service quality statistically, and improve them to some extend. When the service quality is below the threshold, the system will increase its upload bandwidth adaptive to the workload of the system. The supporting system will allocate its bandwidth among multiple overlays to get the as much revenue as possible. This paper presents the design of this architecture and describes its functions and shows its adaptability to the high dynamic nature of p2p IPTV system and fitness for commercial deployment. At last we verify the feasibility of the architecture by simulation, and the results show its performance and adaptability.
9531801
Improving the Performance of Gene Mention Recognition System using Reformed Lexicon-based Support Vector Machine
In this paper, we propose a gene mention recognition system for biomedical literature using Support Vector Machine based on a reformed lexicon. Then we present an ensemble of rule-based post-processing modules, a integrity check module, a boundary check module, an abbreviation resolution module and a name pruning module, to improve the performance further. The newly developed lexicon is composed of uni-indicating and co-indicating words inside gene mention phrases. With the carefully designed lexicon, the characters of gene mentions can be extracted to support the recognition. Based on this lexicon and post-processing modules, our system can recognize gene mentions in biomedical literature with fairly high accuracy, which can achieve the precision of 85.07%, recall of 83.68% and balanced Fβ=1 score of 84.37.
28489429
Transmission Scheduling in Data-Driven Peer-to-Peer Streaming towards Optimal Throughput
Peer-to-peer media streaming has been an important service on the internet in recent years. The Data-driven (or mesh-based) structure is adopted by most working systems,in which data scheduling is one of the important problems.However, those frequently used scheduling algorithms are often faced with such a case: A neighbor peer takes up its bandwidth to deliver the packets that other neighbors can also supply, but some packets only held by it are not delivered.These packets can not be delivered in the current scheduling cycle, even though that the other neighbors have surplus bandwidth. This is a kind of waste of bandwidth and decreases the throughput of transmission. In this paper we propose anew scheduling algorithm aiming at the optimal throughput:Bipartite-matching based Block Scheduling algorithm(BBS).We convert the original data scheduling problem to a problem of finding a maximum match on the correspond bipartite graph, then assign data packets to neighbors according to the maximum match. We evaluate the performance of BBS with extensive experiments and the results show that BBS throughput and provides better streaming quality than those frequently used scheduling algorithms.
7718117
Ensembling Bayesian network structure learning on limited data
In recent years, Bagging method has been applied to learn Bayesian networks (BNs), especially on limited datasets. However, the BNs learned using Bagging method from limited datasets can be biased towards complex models. We present an efficient approach to produce more accurate BNs from limited datasets. Based on the Markov condition of BN learning, we proposed a novel sampling method, called Root Nodes based Sampling (RNS), and a BNs fusion method. The experimental results reveal that our ensemble method can achieve more accurate results in terms of accuracy on limited datasets.