The Internet is composed of many distinct networks, operated by independent Internet Service Providers (ISPs). The traffic and economic relationships of ISPs are mainly decided by their routing policies. However, in today's Internet, overlay routing, which changes traffic routing at the application layer, is rapidly increasing and this challenges the validity of ISPs' existing agreements. We study here the economic implications of overlay routing for ISPs, using an ISP interconnection business model based on a simple network. We then study the overlay traffic patterns in the network under various conditions. Combining the business model and traffic patterns, we study the ISPs' cost reductions with Bill-and-Keep peering and paid peering. We also discuss the ISPs' incentive to upgrade the network under each peering strategy.

A predictive spatio-temporal interval join finds all pairs of moving objects satisfying a join condition on future time interval and space. In this paper, we propose a method called PTJoin. PTJoin partitions the inner index into small sub-trees and performs the join process for each sub-tree to reduce the number of disk page accesses for each window search. Furthermore, to reduce the number of pages accessed by consecutive window searches, we partition the index so that overlapping index pages do not belong to the same partition. Our experiments show that PTJoin reduces the number of page accesses by up to an order of magnitude compared to Interval_STJoin [9], which is the state-of-the-art solution, when the buffer size is small.

Automatic modulation classification (AMC) involves extracting a set of unique features from the received signal. Accuracy and uniqueness of the features along with the appropriate classification algorithm determine the overall performance of AMC systems. Accuracy of any modulation feature is usually limited by the blindness of the signal information such as carrier frequency, symbol rate etc. Most papers do not sufficiently consider these impairments and so do not directly target practical applications. The AMC system proposed herein is trained with probable input signals, and the appropriate decision tree should be chosen to achieve robust classification. Six unique features are used to classify eight analog and digital modulation schemes which are widely used by low frequency mobile emergency radios around the globe. The Proposed algorithm improves the classification performance of AMC especially for the low SNR regime.

An artificial fish swarm algorithm for solving symbolic regression problems is introduced in this paper. In the proposed AFSA, AF individuals represent candidate solutions, which are represented by the gene expression scheme in GEP. For evaluating AF individuals, a penalty-based fitness function, in which the node number of the parse tree is considered to be a constraint, was designed in order to obtain a solution expression that not only fits the given data well but is also compact. A number of important conceptions are defined, including distance, partners, congestion degree, and feature code. Based on the above concepts, we designed four behaviors, namely, randomly moving behavior, preying behavior, following behavior, and avoiding behavior, and present their respective formalized descriptions. The exhaustive simulation results demonstrate that the proposed algorithm can not only obtain a high-quality solution expression but also provides remarkable robustness and quick convergence.

This paper proposes an acoustic modeling technique based on Bayesian framework using multiple model structures for speech recognition. The aim of the Bayesian approach is to obtain good prediction of observation by marginalizing all variables related to generative processes. Although the effectiveness of marginalizing model parameters was recently reported in speech recognition, most of these systems use only “one” model structure, e.g., topologies of HMMs, the number of states and mixtures, types of state output distributions, and parameter tying structures. However, it is insufficient to represent a true model distribution, because a family of such models usually does not include a true distribution in most practical cases. One of solutions of this problem is to use multiple model structures. Although several approaches using multiple model structures have already been proposed, the consistent integration of multiple model structures based on the Bayesian approach has not seen in speech recognition. This paper focuses on integrating multiple phonetic decision trees based on the Bayesian framework in HMM based acoustic modeling. The proposed method is derived from a new marginal likelihood function which includes the model structures as a latent variable in addition to HMM state sequences and model parameters, and the posterior distributions of these latent variables are obtained using the variational Bayesian method. Furthermore, to improve the optimization algorithm, the deterministic annealing EM (DAEM) algorithm is applied to the training process. The proposed method effectively utilizes multiple model structures, especially in the early stage of training and this leads to better predictive distributions and improvement of recognition performance.

Compiling documents in extensible markup language (XML) increasingly requires access to data services which provide both rapid response and the precise use of search engines. Efficient data service should be based on a skillful representation that can support low complexity and high precision search capabilities. In this paper, a novel complete path representation (CPR) associated with a modified inverted index is presented to provide efficient XML data services, where queries can be versatile in terms of predicates. CPR can completely preserve hierarchical information, and the new index is used to save semantic information. The CPR approach can provide template-based indexing for fast data searches. An experiment is also conducted for the evaluation of the CPR approach.

Capacitive feedback VCOs use capacitors that are connected from the output node to the gate of the tail transistor that acts as a current source. Using such feedback results in modulating the current that is used by the oscillator and therefore changes its cyclostationary noise properties which results in a lower output phase noise. This paper presents a mathematical study of capacitive feedback VCOs in terms of stability and phase noise enhancement to confirm stability and to explain the enhancement in phase noise. The derived expression for the phase noise shows an improvement of 4.4 dB is achievable by using capacitive feedback as long as the VCO stays in the current limited region. Measurement results taken from an actual capacitive feedback VCO implemented in a 65 nm CMOS process also agrees with the analysis and simulation results which further validates the given analysis.

Identification of early aspects is the critical problem in aspect-oriented requirement engineering. But the representation of crosscutting concerns is various, which makes the identification difficult. To address the problem, this paper proposes the AspectQuery method based on goal model. We analyze four kinds of goal decomposition models, then summarize the main factors about identification of crosscutting concerns and conclude the identification rules based on a goal model. A goal is crosscutting concern when it satisfies one of the following conditions: i) the goal is contributed to realize one soft-goal; ii) parent goal of the goal is candidate crosscutting concern; iii) the goal has at least two parent goals. AspectQuery includes four steps: building the goal model, transforming the goal model, identifying the crosscutting concerns by identification rules, and composing the crosscutting concerns with the goals affected by them. We illustrate the AspectQuery method through a case study (a ticket booking management system). The results show the effectiveness of AspectQuery in identifying crosscutting concerns in the requirement phase.

In Wireless Sensor Networks (WSNs), sensor nodes consume their limited battery energy to send and receive data packets for data transmission. If some sensor nodes transmit data packets more frequently due to imbalance in the network topology or traffic flows, they experience higher energy consumption. And if the sensor nodes are not recharged, they will be turned off from the lack of battery energy which will degrade network sustainability. In order to resolve this problem, this paper proposes an Energy-aware MAC Protocol (EMP), which adaptively decides on the size of the channel polling cycle consisting of the sleep state (not to communicate with its target node) and the listening state (to awaken to receive data packets), according to the network traffic condition. Moreover, in accordance with the remaining energy state of the sensor node, the minimum size of the channel polling cycle is increased for better energy saving. For performance evaluation and comparison, we develop a Markov chain-based analytical model and an event-driven simulator. Simulation results show that a sensor node with EMP effectively reduces its energy consumption in imbalanced network condition and traffic flows, while latency somewhat increases under insufficient remaining energy. As a consequence, a holistic perspective for enhanced network sustainability can be studied in consideration of network traffic condition as well as the remaining energy states of sensor nodes.

In this study, a novel forest method based on specific random trees (SRT) was proposed for a multiclass classification problem. The proposed SRT was built on one specific class, which decides whether a sample belongs to a certain class. The forest can make a final decision on classification by ensembling all the specific trees. Compared with the original random forest, our method has higher strength, but lower correlation and upper error bound. The experimental results based on 10 different public datasets demonstrated the efficiency of the proposed method.

In this paper, a symbol-rate clock recovery scheme for a receiver that uses an integrating decision feedback equalizer (DFE) is proposed. The proposed clock recovery using expected received signal amplitudes as the criterion realizes minimum mean square error (MMSE) clock recovery. A receiver architecture using an integrating DFE with the proposed symbol-rate clock recovery is also proposed. The proposed clock recovery algorithm successfully recovered the clock phase in a system level simulation only with a DFE. Higher jitter tolerance than 0.26 UIPP at 10 Gb/s operation was also confirmed in the simulation with an 11 dB channel loss at 5 GHz.

In recent years, technologies and markets related to data centers have been rapidly changing and growing. Data centers are playing an important role in ICT infrastructure deployment and promise to become common platforms for almost all social infrastructures. Even though research has focused on networking technologies, various technologies are needed to develop high-performance, cost-efficient, and flexible large-scale data centers. To understand those technologies better, this paper surveys recent research and development efforts and results in accordance with a data center network taxonomy that the authors defined.

In complex network analysis, there are various measures to characterize the centrality of each node within a graph, which determines the relative importance of each node. The more centrality a node has in a network, the more significance it has in the spread of infection. As one of the important extensions to shortest-path based betweenness centrality, the flow betweenness centrality is defined as the degree to which each node contributes to the sum of maximum flows between all pairs of nodes. One of the drawbacks of the flow betweenness centrality is that its time complexity is somewhat high. This Letter proposes an approximate method to calculate the flow betweenness centrality and provides experimental results as evidence.

Refinement methods for landmark detection and extraction of articulator-free features for a knowledge-based speech recognition system are described. Sub-band energy difference profiles are used to detect landmarks, with additional parameters used to improve accuracy. For articulator-free feature extraction, duration, relative energy, and silence detection are additionally used to find [continuant] and [strident] features. Vowel, obstruent and sonorant consonant landmarks, and locations of voicing onsets and offsets are detected within a unified framework with 85% accuracy overall. Additionally, 75% and 79% of [continuant] and [strident] features, respectively, are detected from landmarks.

This paper deals with two types of capacity allocation schemes, i.e., static and adaptive, for uplink and downlink burst durations in the IEEE 802.16 BE (Best Effort) service. We study QoE (Quality of Experience) enhancement of audio-video IP transmission over the uplink channel with the two capacity allocation schemes. We introduce a piggyback request mechanism for uplink bandwidth requests from subscriber stations to the base station in addition to a random access-based request mechanism. We assess QoE of audio-video streams for four schemes obtained from the combination of the capacity allocation schemes and the bandwidth request mechanisms. We also employ two types of audio-video contents. From the assessment result, we notice that the adaptive allocation scheme is effective for QoE enhancement particularly under heavily loaded conditions because of its efficient usage of OFDM symbols. In addition, the piggyback request mechanism can enhance QoE of audio-video transmission. We also find that the effects of capacity allocation schemes and piggyback request mechanism on QoE change according to the content types.

In this letter, we propose a new QoS scheduling algorithm, referred to as the explicit traffic aware scheduling algorithm with explicit queue length notification (ETA-EQN), which aims at supporting multimedia services in HCCA of IEEE 802.11e WLAN. The results from intensive simulations with NS-2 verify that ETA-EQN provides much better network performance than the reference scheduler in terms of throughput, delay, and packet loss.

Reed-Solomon (RS) code is one of the well-known and widely used error correction codes. Among the components of a hardware RS decoder, the key equation solver (KES) unit occupies a relatively large portion of the hardware. It is important to develop an efficient KES architecture to implement efficient RS decoders. In this paper, a novel polynomial division technique used in the Euclidean algorithm (EA) of the KES is presented which achieves the short critical path delay of one Galois multiplier and one Galois adder. Then a KES architecture with the EA is proposed which is efficient in the sense of the product of area and time.

In this paper, we propose a high-speed full-duplex optical wireless communication system using a single channel imaging receiver for personal area network applications. This receiver is composed of an imaging lens, a small sensitive-area photodiode, and a 2-aixs actuator and it can reject most of the background light. Compared with the previously proposed system with single wide field-of-view (FOV) non-imaging receiver, the coverage area at 12.5 Gb/s is extended by > 20%. Furthermore, since the rough location information of the user is available in our proposed system, instead of searching for the focused light spot over a large area on the focal plane of the lens, only a small possible area needs to be scanned. In addition, by pre-setting a proper comparison threshold when searching for the focused light spot, the time needed for searching can be further reduced. Proof-of-concept experiments have been carried out and the results show that with this partial searching algorithm and pre-set threshold, better performance is achieved.

With the wide-spread use of high-speed network connections and high performance mobile/sensor terminals available, new interactive services based on real-time contents have become available over the Internet. In these services, end-nodes (e.g, smart phone, sensors), which are dispersed over the Internet, generates the real-time contents (e.g, live video, sensor data about human activity), and those contents are utilized to support many kinds of human activities seen in the real world. For the services, a new decentralized contents distribution system which can accommodate a large number of content distributions and which can minimize the end-to-end streaming delay between the content publisher and the subscribers is proposed. In order to satisfy the requirements, the proposed content distribution system is equipped with utilizing two distributed resource selection methods. The first method, distributed hash table (DHT)-based contents management, makes it possible for the system to efficiently decide and locate the server managing content distributions in completely decentralized manner. And, the second one, location-aware server selection, is utilized to quickly select the appropriate servers that distribute the streamed contents to all subscribers in real time. This paper considers the performance of the proposed resource selection methods using a realistic computer simulation and shows that the system with the proposed methods has scalability for a large-scale distributed system that attracts a very large number of users, and achieves real-time locating of the contents without degrading end-to-end streaming delay of content.

This letter proposes a new mechanism for network configuration on a mobile device that provides Point of Attachment (PoA) specific information using IEEE 802.21 and DHCP before moving to a new PoA. This allows the mobile device to prepare for intelligent handover decision either stateless address configuration or stateful address configuration when entering an IPv6 network. It allows the mobile device to reduce time delay for IP address configuration in the new PoA. Implementation and evaluation results show that the proposed mechanism can be an acceptable network configuration mechanism for providing seamless television watching in IPv6 mobile networks, even when in motion.