Online algorithm for application layer anycast

Application layer anycast possesses the property of flexibility, however, all the proposed application layer anycast routing algorithms are based on probing so far. One disadvantage of probing algorithms is that there are too many probing packets, which degrade the network performance, wast network bandwidth. In this paper, we propose an online algorithm, balance algorithm, for application layer anycast. Compared with the probing algorithms, the proposed algorithm has no probing cost at all. We model the two kinds of algorithms, and analyse the performance of the two algorithms. The results show that the online balance algorithm is better than the probing algorithms in terms of performance. A simulation is conducting as future work.

Online mining of frequent sets in data streams with error guarantee

For most data stream applications, the volume of data is too huge to be stored in permanent devices or to be thoroughly scanned more than once. It is hence recognized that approximate answers are usually sufficient, where a good approximation obtained in a timely manner is often better than the exact answer that is delayed beyond the window of opportunity. Unfortunately, this is not the case for mining frequent patterns over data streams where algorithms capable of online processing data streams do not conform strictly to a precise error guarantee. Since the quality of approximate answers is as important as their timely delivery, it is necessary to design algorithms to meet both criteria at the same time. In this paper, we propose an algorithm that allows online processing of streaming data and yet guaranteeing the support error of frequent patterns strictly within a user-specified threshold. Our theoretical and experimental studies show that our algorithm is an effective and reliable method for finding frequent sets in data stream environments when both constraints need to be satisfied.

Discovering frequent sets from data streams with CPU constraint

Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredictable rates, and fast changing data characteristics. It has been hence recognized that mining over streaming data requires the problem of limited computational resources to be adequately addressed. Since the arrival rate of data streams can significantly increase and exceed the CPU capacity, the machinery must adapt to this change to guarantee the timeliness of the results. We present an online algorithm to approximate a set of frequent patterns from a sliding window over the underlying data stream - given apriori CPU capacity. The algorithm automatically detects overload situations and can adaptively shed unprocessed data to guarantee the timely results. We theoretically prove, using probabilistic and deterministic techniques, that the error on the output results is bounded within a pre-specified threshold. The empirical results on various datasets also confirmed the feasiblity of our proposal.

Energy minimization in multi-task software-defined sensor networks

Abstract—
After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies makes it practical to realize the software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. A sensor node in SDSN is able to conduct multiple tasks with different sensing targets simultaneously. A given sensing task usually involves multiple sensors to achieve a certain quality-of-sensing, e.g., coverage ratio. It is significant to design an energy-efficient sensor scheduling and management strategy with guaranteed quality-of-sensing for all tasks. To this end, three issues are investigated in this paper: 1) the subset of sensor nodes that shall be activated, i.e., sensor activation, 2) the task that each sensor node shall be assigned, i.e., task mapping, and 3) the sampling rate on a sensor for a target, i.e., sensing scheduling. They are jointly considered and formulated as a mixed-integer with quadratic constraints programming (MIQP) problem, which is then reformulated into a mixed-integer linear programming (MILP) formulation with low computation complexity via linearization. To deal with dynamic events such as sensor node participation and departure, during SDSN operations, an efficient online algorithm using local optimization is developed. Simulation results show that our proposed online algorithm approaches the globally optimized network energy efficiency with much lower rescheduling time and control overhead.

Opportunistic offloading of deadline-constrained bulk cellular traffic in vehicular DTNs

The ever-growing cellular traffic demand has laid a heavy burden on cellular networks. The recent rapid development in vehicle-to-vehicle communication techniques makes vehicular delay-tolerant network (VDTN) an attractive candidate for traffic offloading from cellular networks. In this paper, we study a bulk traffic offloading problem with the goal of minimizing the cellular communication cost under the constraint that all the subscribers receive their desired whole content before it expires. It needs to determine the initial offloading points and the dissemination scheme for offloaded traffic in a VDTN. By novelly describing the content delivery process via a contact-based flow model, we formulate the problem in a linear programming (LP) form, based on which an online offloading scheme is proposed to deal with the network dynamics (e.g., vehicle arrival/departure). Furthermore, an offline LP-based
analysis is derived to obtain the optimal solution. The high efficiency of our online algorithm is extensively validated by simulation results.

A co-recommendation algorithm for web searching

This paper presents an approach called the Co-Recommendation Algorithm, which consists of the features of the recommendation rule and the co-citation algorithm. The algorithm addresses some challenges that are essential for further searching and recommendation algorithms. It does not require users to provide a lot of interactive communication. Furthermore, it supports other queries, such as keyword, URL and document investigations. When the structure is compared to other algorithms, the scalability is noticeably easier. The high online performance can be obtained as well as the repository computation, which can achieve a high group-forming accuracy using only a fraction of Web pages from a cluster.

EStream: online mining of frequent sets with precise error guarantee

In data stream applications, a good approximation obtained in a timely  manner is often better than the exact answer that’s delayed beyond the window of opportunity. Of course, the quality of the approximate is as important as its timely delivery. Unfortunately, algorithms capable of online processing do not conform strictly to a precise error guarantee. Since online processing is essential and so is the precision of the error, it is necessary that stream algorithms meet both criteria. Yet, this is not the case for mining frequent sets in data streams. We present EStream, a novel algorithm that allows online processing while producing results strictly within the error bound. Our theoretical and experimental results show that EStream is a better candidate for finding frequent sets in data streams, when both constraints need to be satisfied.

A stable MCA learning algorithm

Minor component analysis (MCA) is an important statistical tool for signal processing and data analysis. Neural networks can be used to extract online minor component from input data. Compared with traditional algebraic  approaches, a neural network method has a lower computational complexity. Stability of neural networks learning algorithms is crucial to practical applications. In this paper, we propose a stable MCA neural networks learning algorithm, which has a more satisfactory numerical stability than some existing MCA algorithms. Dynamical behaviors of the proposed algorithm are analyzed via deterministic discrete time (DDT) method and the conditions are obtained to guarantee convergence. Simulations are carried out to illustrate the theoretical results achieved.

Online audio background determination for complex audio environments

We present a method for foreground/background separation of audio using a background modelling technique. The technique models the background in an online, unsupervised, and adaptive fashion, and is designed for application to long term surveillance and monitoring problems. The background is determined using a statistical method to model the states of the audio over time. In addition, three methods are used to increase the accuracy of background modelling in complex audio environments. Such environments can cause the failure of the statistical model to accurately capture the background states. An entropy-based approach is used to unify background representations fragmented over multiple states of the statistical model. The approach successfully unifies such background states, resulting in a more robust background model. We adaptively adjust the number of states considered background according to background complexity, resulting in the more accurate classification of background models. Finally, we use an auxiliary model cache to retain potential background states in the system. This prevents the deletion of such states due to a rapid influx of observed states that can occur for highly dynamic sections of the audio signal. The separation algorithm was successfully applied to a number of audio environments representing monitoring applications.

An efficient least common subgraph algorithm for video indexing

Many tasks in computer vision can be expressed as graph problems. This allows the task to be solved using a well studied algorithm, however many of these algorithms are of exponential complexity. This is a disadvantage when considered in the context of searching a database of images or videos for similarity. Work by Mesaner and Bunke (1995) has suggested a new class of graph matching algorithms which uses a priori knowledge about a database of models to reduce the time taken during online classification. This paper presents a new algorithm which extends the earlier work to detection of the largest common subgraph.

A globally convergent MC algorithm with an adaptive learning rate

This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.

Growing self-organizing map for online continuous clustering

The internet age has fuelled an enormous explosion in the amount of information generated by humanity. Much of this information is transient in nature, created to be immediately consumed and built upon (or discarded). The field of data mining is surprisingly scant with algorithms that are geared towards the unsupervised knowledge extraction of such dynamic data streams. This chapter describes a new neural network algorithm inspired by self-organising maps. The new algorithm is a hybrid algorithm from the growing self-organising map (GSOM) and the cellular probabilistic self-organising map (CPSOM). The result is an algorithm which generates a dynamically growing feature map for the purpose of clustering dynamic data streams and tracking clusters as they evolve in the data stream.

Genetic algorithm based PI controller tuning for induction motor drive with ANN flux estimator

This paper presents a Genetic Algorithm (GA) based fast speed response controller for poly-phase induction motor drive. Here the proportional and integral gains of PI controller are optimized by GA to achieve quick speed response. An adaptive Recurrent Neural Network (RNN) with Real Time Recurrent Learning (RTRL) algorithm is proposed to estimate rotor flux. An online tuning scheme to update the weight of RNN is presented to overcome stator resistance variation problem. This tuning scheme requires torque estimator to calculate the torque error. Space vector modulation (SVM) technique is used to produce the motor input voltage. Simulation tests have been performed to study the dynamic performances of the drive system for both the classical PI and the genetic algorithm based PI controllers.

An online transfer learning RBF neural network for cross domain data classification

In this paper, a Radial Basis Function Network (RBFN) trained with the Dynamic Decay Adjustment (DDA) algorithm (i.e., RBFNDDA) is deployed as an incremental learning model for tackling transfer learning problems. An online learning strategy is exploited to allow the RBFNDDA model to transfer knowledge from one domain and applied to classification tasks in a different yet related domain. An experimental study is carried out to evaluate the effectiveness of the online RBFNDDA model using a benchmark data set obtained from a public domain. The results are analyzed and compared with those from other methods. The outcomes positively reveal the potentials of the online RBFNDDA model in handling transfer learning tasks. © 2014 The authors and IOS Press. All rights reserved.

Adaptive washout algorithm based fuzzy tuning for improving human perception

The aim of this paper is to provide a washout filter that can accurately produce vehicle motions in the simulator platform at high fidelity, within the simulators physical limitations. This is to present the driver with a realistic virtual driving experience to minimize the human sensation error between the real driving and simulated driving situation. To successfully achieve this goal, an adaptive washout filter based on fuzzy logic online tuning is proposed to overcome the shortcomings of fixed parameters, lack of human perception and conservative motion features in the classical washout filters. The cutoff frequencies of highpass, low-pass filters are tuned according to the displacement information of platform, workspace limitation and human sensation in real time based on fuzzy logic system. The fuzzy based scaling method is proposed to let the platform uses the workspace whenever is far from its margins. The proposed motion cueing algorithm is implemented in MATLAB/Simulink software packages and provided results show the capability of this method due to its better performance, improved human sensation and exploiting the platform more efficiently without reaching the motion limitation.