bluuebunny/crossref_metadata_2025_split huggingface dataset:
AHLSS: A Hierarchical, Adaptive, Extendable P2P Live Streaming System
Authors: Li Runzhi , Guo Chunyi , Fan Ming , Wang Zongmin 
Published: January 2009

With the development of P2P streaming technology, various live streaming systems have been designed. Some measurement studies recently have shown that the most important issue exposed by previous systems is how to balance the benefits between the internet service provider, the internet content provider, and P2P users. This issue is induced by little consideration on management and control. Other problems include how to resolve peers churn and make full use of network bandwidth and apply incentive mechanism among peers. In this work we introduce AHLSS, a P2P system for live streaming which is hierarchical, adaptive, and extendable. We present the architecture of AHLSS which is divided into three layers. The top layer named AP (Administrator Peer) overlay consisted of some authentic servers, which are arranged by ICP (Internet Content Provider) to provide enrollment and authentication for peers to join P2P overlay and start to watch a media channel. Every AP locates in an alliance alone. The middle layer is SP overlay. AHLSS divides peers that participate in data transmission into two classes, including SP and NP, which base on the capability of peers. SP is elected by AP. SP has responsibility to route media packets quickly and reply peers' channel request within the cluster. The under layer consists of many clusters. A cluster is formed by uniting peers which have poor capability and little stability near SP. SP is named cluster-head with the responsibility for management of members in the cluster. Our system's innovative features are the following: It provides the function of enrollment and authentication; AP look up peer's EnrollInfo (a list, which keeps the register information of peers) periodically and apply a decision-tree approach to elect SP. It resolves problems which are associated with the dynamics and heterogeneity of peers; to cope with the problem of topology-mismatch, we design Alliance and Cluster to partition the physical location of peers and make use of the Two-hop Detection Algorithm which belongs to LTM technology to optimize the connection of SP among alliances; different transmission modes are designed on SP overlay and within cluster. Native mesh-pull is adopted in SP overlay and the hybrid mode includes tree-push and mesh-pull which are applied in NP (Normal Peer) overlay. It reduces the delay in chunks transmitted through SP to NP and makes full use of the upload bandwidth of peers; Approach of K degree redundancy cluster-head is adopted by NP, it is used to avoid the inorganized status for NPs which resulted from SP's failure. In this paper, we describe in detail the main algorithms that stand behind the design of AHLSS and present the analysis of architecture for various aspects including adaptability, scalability, efficiency, and robustness.
IAM: An Effective and Robust Protocol for P2P Live Streaming System
Authors: Guo Hongfang , Lin Yusong , Wang Zongmin 
Published: January 2009

The phenomenal success and popularity of peer-to-peer (p2p) streaming over the last decade took many providers, researchers, as well as the mass, by surprise. It would become the dominant type of traffic over the Internet in the very near future. The released Windows Vista contains its own P2P networking system. Though P2P streaming has become one of the most attractive network applications, it is still awaiting some technological breakthroughs. P2P streaming faces very different challenges from other P2P applications, such as file download. In live P2P streaming, the stringent real-time performance requirement in terms of bandwidth and latency is one of the challenges it faces. In addition, frequent joining and leaving of the autonomous end-hosts have more significant impact on the system performance. So the churn problem and reconstruction due to uncertainty are key challenges of P2P streaming. A number of solutions were proposed which did not take all the factors into account. This paper presents IAM (IP Address Matching), a novel protocol for P2P live streaming system. For overlay structure, neighbor selection algorithm-FAMM (Forward Address Maximum Matching), which allows the peer choose the close peers as neighbors, is proposed. In this mechanism peer selects neighbors by matching IP address in regular expression. IP address is assigned by area basically, so the closer the peers are, the more the same bits of IP address they have. Peer chooses the first m (m > 0) peers whose IP address has same bits with it as the neighbors. Meanwhile, the latency is generally low. FAMM just likes the DHT routing. As managing peer failures and proper recovery are of critical importance for a robust P2P streaming system, a new peer exception mechanism is also proposed which is the other central issue of the IAM. According to the different error information which the peer receives, it detects the departure or failure of the neighbor intelligently and recovers from an unstable state promptly. To evaluate the performance of the IAM, we have conducted extensive simulations and real-network experiment. The results indicate that IAM shortens the latency and is robust to the dynamic peers. It achieves superior comprehensive performance over Random scheme. It is proved effective in the real-network environment, and the latency of recovery is shortened significantly which is only 25% of that with the usual mechanism, as well.
Fast Inter Mode Decision Based on RD Costs and Frequencies of Modes
Authors: Li Wei , Zhou Bing , Huang Xueli 
Published: January 2009

The new video coding standard H.264/AVC achieves higher coding efficiency than previous standards. However, the efficiency arises at the cost of significant complexity. In this paper, an efficient intermode decision algorithm is proposed to cut down the complexity of an exhaustively full mode decision algorithm in the reference software. Statistical variables including RD (Rate Distortion) costs and frequencies of various modes, correlation of modes between current MB (Macroblock), and their spatial and temporal neighbor MBs are utilized to achieve fast mode decision in the algorithm. The proposed algorithm is composed of three primary steps: The characteristics information of video content, RD cost, and frequency of each mode, are obtained by statistics to assist the decision of the best coding mode for the MB. Because the accuracy of the statistical results will affect the succedent mode decision process, FMD (Full Mode Decision) algorithm are used to get the statistical values from several training frames. In addition, considering the above statistical variables change with video content's changes, they are updated at regular intervals. The best coding modes of current MBs and their neighbor MBs (both spatial and temporal) are highly correlated, so the coding mode of these neighbor MBs are used as an indication to that of current MBs. The difference of RD costs between MB mode (16×16, 16×8 and 8×16) and non-MB modes is used to decide the possible modes class for current MB. Furthermore, the possible modes are prioritized based on their occurring probabilities such that the highest probable mode will be tried first. During this process, the computed RD cost is checked against a content adaptive RD cost threshold to decide if the mode decision process should be terminated before trying the remaining modes in the class. In this way many unlikely coding modes are skipped, and the computational time is significantly reduced. Experimental results show that the proposed algorithm performs well on both the low motion sequences and the high motion sequences due to utilizing the sequence-dependent statistical variables as adaptive threshold to decide the best coding modes. On average, the algorithm reduces the total encoding time by 66.3% while image quality (PSNR) degradation is only 0.154dB with bitrate increase about 0.21%.
The Aide Diagnosis of Cardiac Heart Diseases Using a Deoxyribonucleic Acid Based Backpropagation Neural Network
Authors: Shi Li , Wang Zhizhong , Wang Lijia , Zhang Jinying 
Published: January 2009

In this paper, a novel diagnostic scheme of cardiac heart diseases is presented using a Deoxyribonucleic Acid (DNA)-based BP(DNA-BP) Neural Network by distinguishing the shapes of ST segments automatically. First, wavelet transform is applied to extract the ST segments by identifying the characteristic points in the ECG. ECG signals are decomposed by aTrous Algorithm using dyadic spline wavelets. The relationship between the feature points of ECG signals and the modulus maximum pairs of the signals' wavelet transform is established, and the R-wave and ST segment's fiducial points are extracted at different wavelet scales. Second, in order to overcome the disadvantages of the BP neural network, a DNA optimization method is adopted to optimize the original weights and bias of a BP neural network. The BP algorithm is used to find the most optimal values of the weights and bias of the BP network. At last the effectiveness of the proposed aided diagnostic scheme is demonstrated via the experiments which the data are from the clinical study and MIT/BIH ECG data base. In order to validate the advantages of the DNA-based BP (DNA-BP) network and determine the best application conditions of the network, two types of experiments were conducted. In each experiment, 30 samples were selected as the training data and testing data respectively. In the first type of experiments, the data were obtained from the ECG of different people. While in the second type of experiments, the data were obtained from the ECG of one person at different times. The same data and the conditions were used in both BP and GA-based BP (GA-BP) networks to illustrate the advantages of the proposed DNA-based BP network. The experiment results illustrate that the proposed DNA-based BP network overcomes the limitation of the sloping method in identifying straight line ST segments and the limitation of function fitting method in fitting accuracy. It also surmounts the disadvantages of a BP network in terms of the local minimum, slow convergence, and the shortcomings of GA-BP in terms of its limitation in algorithm coding and evolution ways. In conclusion, the neural network is used to supply an invaluable aid for physicians to diagnose cardiac heart diseases and myocardial infarction.
A Novel Filter Bank Design Method with CEM FRM Technique
Authors: Zhang Li-Zhi , Luo Wen-Yu 
Published: January 2009

Modulation filter banks can be obtained by modulating one or two prototype filters, which reduce the design complexity of the filter banks. Furthermore, this kind of filter can be realized by using DFT or FFT with very high computationally efficiency. The proposition of a modulation filter bank reduces the computation complexity of designing the filter bank consumedly, which has very high computation efficiency. However, with the increasing of channels, the request for transition of the prototype filter is very high. This makes the length of the prototype filter longer, which decreases the computation efficiency. Frequency-response masking technique, which was proposed by Lim in 1986, is a very efficient way for designing the sharp transition filter. It is composed of three sub-filters and a delay chain. The basic thought is designing a low complexity filter before compressing its frequency response, and then eliminating the useless periodic images of the prototype filter and its compensation filter by two masking filters. The complexity of the FRM filter can also be reduced by modifying its architecture. According to it, the paper deduces a Novel FRM low-pass filter based on Complex Exponential Modulation (CEM). The deduced filter is very suitable to the multi-rate digital signal process, apart from applying a single rate digital signal process. In the application of software defined radio, the most bandwidth needs to be separated because it may has many narrowband channels. The need is very stringent for the filter to attenuate the closer channels. The filter banks encounter a main problem when it is used to separate channels, which is that the prototype filter must be a sharp transition bandwidth filter. Furthermore, the length of the prototype filter becomes longer in the DFT filter banks when the number of sub-filters is large and the transition band is very sharp. So it is very important to use a high efficiency digital filter, which can reduce the number of coefficient observably. Based on the advantage of deduced novel low-pass filter, the paper proposes a new analysis filter bank architecture, which makes use of the high efficiency low-pass filter as a prototype filter. So it can decrease the computation complexity consumedly when the transition bandwidth of prototype filter is especially very sharp. It is the same with the alterable bandwidth system. At last, an eight channels filter bank design example is used to illustrate its feasibility. It reduces the computationally complexity of the sharp transition filter by using a CEM FRM prototype filter. At last, an application example has been given to illustrate the design of analysis filter banks, showing that the resulting filter bank reduces the computation complexity, which adapt to use in engineering implementation.
On the Approximation of Fuzzy System
Authors: Yue Shihong , Chen Wei , Wang Jin , Wang Huaxiang 
Published: January 2009

The choice of fuzzy set function affects how well the fuzzy systems approximate an unknown function. We first examine several popular fuzzy systems, based on which we generalize the error of the fuzzy system by the mathematical functional. Two classes of extended fuzzy systems with special set function are proposed respectively in one-dimensional and two-dimensional reproducing kernel spaces, and they are in fact the best interpolation operators in the sense of classical mathematical approximation. The extended fuzzy system can be used as easily as any typical fuzzy system but has better characters. Our major contributions are as follows. We prove that the existing fuzzy system with a triangle-shaped set function is by no means the best choice for an uncertain system. On the contrary, the reproducing kernel-shaped set function can give the best approximation in the sense of mathematical functional. According to the result, overlapped symmetric triangles or trapezoid set functions reduce fuzzy systems to piecewise linear systems. Universal Gaussian bell-curve set functions can work well but its learning cost is too expensive to apply under online condition. We explain what our proposed extended fuzzy system is useful for a given uncertain system. This solution is associated with another problem: Is the reproducing kernel condition too specific to limit its applications? We must stress that the reproducing kernel function is easily found in any continuous function space. Consequently, the extended fuzzy system can approximate any continuous function with arbitrary accuracy. Furthermore one may question why we do not directly use an optimal approximation functional instead of the extended fuzzy system. Here we answer this problem by the following point of views: The two methods face different applicable conditions. The optimal approximation functional is used to interpolate those accurate data. These data are needed under certain conditions such as distributions and quantity, etc. When little information is available, an optimal approximation functional will do nothing. But the extended fuzzy system gives a good approximation to an uncertain system and can work efficiently under the condition of vague or poor information. Consequently, the two methods have different optimal criteria and different input and output descriptions. Furthermore, the extended fuzzy systems can be created flexibly by artificial experiences or other ways. Two experiments are used to verify the effectiveness and efficiency of our proposed extended fuzzy systems.
A Rule-Based Prefetching Approach for Object-Based Storage Device
Authors: He Shuibing , Feng Dan , Li Chunhua , Yuan Yanli 
Published: January 2009

Capturing the advantages of NAS and SAN, Object-based Storage System (OBSS) is becoming an active research area. OBSS is composed of clients, metadata server, and Object-based Storage Devices (OSDs). Similar to the prefetching of file and block, object prefetching is also an effective method to reduce the object access latency in OSD. However, the existing methods focus on intra-object prefetching [ 1 ] and they limit the prefetching capability among objects to a certain extent. In this paper, we present a novel inter-object prefetching approach which considers the characteristics of the object access in OSD. This approach adopts a kind of data mining technique called sequential pattern mining to find the object access sequential patterns [ 2 ], then produces the object sequential rules according to the patterns, and finally prefetches objects according to the rules and the object access context. The whole process includes the following three modules: Object Request Service Module. It maintains a current object access context, which is a l-length recently accessed object sequence. Furthermore, it appends new object information to the historic object access stream when an object is accessed. Sequential Rules Mining Module. It is composed of the object sequences preprocessing, the sequential pattern mining and the sequential rules producing modules. The preprocessing module divides the object access stream into many subsequences. Base on the discovered object sequential patterns gained by the sequential pattern mining module, object access sequential rules table is constructed. Prefetching Engine Module. When the current requested object is not in the object cache, the prefetching engine selects a matched rule with the highest confidence from the rules table according to the context, then chooses the corresponding objects and preloads them into the object cache. Simulated experimental results show that both prediction accuracy and object hit rate can be improved comparing with the Group-based prefetching method [ 3 ] which can also be used for object prefetching.
A Novel Spatio-Temporal Attributes Index Based Query for Wireless Sensor Networks
Authors: Wu Weiguo , Chen Heng , Wu Yong , Liu Yi 
Published: January 2009

Wireless sensor networks (WSNs) are envisioned to consist of hundreds to thousands of wireless sensor nodes. The operator doesn't interest in the data sensed by a specific sensor node generally, on the contrary, he pays more attention to the data gathered from a specific area in granted time. One crucial problem is how to process the great deal of data and respond to the query request. We propose a novel query processing based data attributes, called Spatio-Temporal Attributes R-tree based Query (STARQ). Consider the similarity of data collected by a sensor node and its neighboring nodes, partial clustering algorithm is used to form a storage cluster. Partial clustering algorithm is implemented in two phases. First phase is the beginning of partial clustering, in which an object occurs. In second phase, a certain node (e.g., resumes from failure) senses an existed object. The method provided in this paper aims to obtain the neighboring nodes firstly, and judges whether existing a storage cluster that conforms to metadata of the sensor node or not. If the relevant storage cluster does not exist, first phase works, otherwise second phase. If failed in first phase, partial clustering algorithm is called again after a random time. If there are more than one relevant storage cluster in second phase, exercises a sort algorithm which is in descending order according to the storage node's capability weight, and tries to join a storage cluster in turn. R-tree [ 1 ] is an approximately balanced search tree that is widely used to handle spatial data in traditional database systems. Motivated by the unique characteristic of R-tree, a Saptio-Temporal Attributes R-tree (STAR) is built on the top of storage clusters. Objects in STAR are not restricted to the geographical rectangles and could be any abstract ranges of arbitrary attributes. A top-down approach that achieves energy efficiency is adopted to locate the corresponding storage nodes, which transmit the relevant data back to the operator. We compare STARQ with Directed Diffusion [ 2 ] and GHT [ 3 ] in NS-2. To measure the performance of these protocols, we consider two metrics: interval of query and the size of network. The simulation results show that STARQ has better performance with different query intervals and network size.
Improving WSN Base Station Survivability with Rejuvenation Technology
Authors: Xin He , Xiaolin Gui , Wei Wei 
Published: January 2009

The conventional security technology for WSN is mainly focused on improvement to the system's anti-invasion ability; however, seldom concerns how the system should go on providing external services after the invasion and failure, leading to the system's low survivability. In addition, base station is a keyport for WSN. Therefore, software rejuvenation technology is introduced into the construction of a survival model for WSN base station, in order to improve the overall WSN survival performance. The model includes the monitor, the analysis unit, the rejuvenation operation unit, the evaluation unit, strategy Database, etc. Its operation principles are as follows: first, the monitor can gets context information of the system recourses through agents (software sensors), and makes prompt examination and data collecting. Then, the analysis unit will make policy on the collected data, and judge the existence cause and harm degree of abnormal resource consumption by means of knowledge provided by strategies database, in order to perform the rejuvenation operation in time. When the rejuvenation occurs, it is necessary to clear the internal status (e.g., trash recycling, refreshing operation system tables, reinitializing internal data construction etc., adopting different operations in the presence of different strategies), to cut off exterior malicious links, to release system resources, and to restore system performance. Besides, when the rejuvenation operation is performed, the reconfiguration operation can also be adopted in order that patches are put on according to the system loophole, to avoid wrong reappearance caused by system interior drawbacks or the continuous occurrence of the same exterior attacks. Finally, according to the restored system performance, an evaluation of the original strategy which influenced the operation this time should be made and recorded in the strategies database by the evaluation unit, making the frame model adjust itself to a varied environment. Thus, the uninterrupted services can be provided by the system in face of attack or failure by the model, which monitors system performance to predict the rejuvenation interval and performs rejuvenation operation. Otherwise, the model is described by using the Semi-Markov Process and it is discovered that the detection probability of the system compromised state is the key parameter of improving the model's survivability by analyzing the model's stable health state probability. Too convienient for simulation, the base station survival model is described with Stochastic reward network and the experimental results gained by simulation tool SPNP show that the system detection probability is in inverse proportion of the failure probability. According to thought of rejuvenation technology, the failure probability can be reduced significantly by improving system detection probability so as to enhance the survivability of the system, and further improve the overall survival performance of WSN.