Qilian Liang

Interval type-2 fuzzy logic systems: theory and design

We present the theory and design of interval type-2 fuzzy logic systems (FLSs). We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs: one that is based on a general inference formula for them. We introduce the concept of upper and lower membership functions (MFs) and illustrate our efficient inference method for the case of Gaussian primary MFs. We also propose a method for designing an interval type-2 FLS in which we tune its parameters. Finally, we design type-2 FLSs to perform time-series forecasting when a nonstationary time-series is corrupted by additive noise where SNR is uncertain and demonstrate an improved performance over type-1 FLSs.

Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters

Presents a kind of adaptive filter: type-2 fuzzy adaptive filter (FAF); one that is realized using an unnormalized type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS). We apply this filter to equalization of a nonlinear time-varying channel and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A clustering method is used to adaptively design the parameters of the FAF. Two structures are used for the equalizer: transversal equalizer (TE) and decision feedback equalizer (DFE). A decision tree structure is used to implement the decision feedback equalizer, in which each leaf of the tree is a type-2 FAF. This DFE vastly reduces computational complexity as compared to a TE. Simulation results show that equalizers based on type-2 FAFs perform much better than nearest neighbor classifiers (NNC) or equalizers based on type-1 FAFs.

Silent Positioning in Underwater Acoustic Sensor Networks

In this paper, we present a silent positioning scheme termed UPS for underwater acoustic sensor networks. UPS relies on the time difference of arrivals locally measured at a sensor to detect range differences from the sensor to four anchor nodes. These range differences are averaged over multiple beacon intervals before they are combined to estimate the 3-D sensor location through trilateration. UPS requires no time synchronization and provides location privacy at underwater vehicles/sensors whose locations need to be determined. To study the performance of UPS, we model the underwater acoustic channel as a modified ultrawideband Saleh-Valenzuela model: The arrival of each path cluster and the paths within each cluster follow double Poisson distributions, and the multipath channel gain follows a Rician distribution. Based on this channel model, we perform both theoretical analysis and simulation study on the position error of UPS under acoustic fading channels. The obtained results indicate that UPS is an effective scheme for underwater vehicle/sensor self-positioning.

Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems

This paper presents a connection admission control (CAC) method that uses a type-2 fuzzy logic system (FLS). Type-2 FLSs can handle linguistic uncertainties. The linguistic knowledge about CAC is obtained from 30 computer network experts. A methodology for representing the linguistic knowledge using type-2 membership functions and processing surveys using type-2 FLS is proposed. The type-2 FLS provides soft decision boundaries, whereas a type-1 FLS provides a hard decision boundary. The soft decision boundaries can coordinate the cell loss ratio (CLR) and bandwidth utilization, which is impossible for the hard decision boundary.

MPEG VBR video traffic modeling and classification using fuzzy technique

We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of T/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to classify video traffic using compressed data. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier in which the input is modeled as a type-2 fuzzy set and antecedent membership functions are modeled as type-2 fuzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent algorithm is used to tune its parameters.

Sparse target counting and localization in sensor networks based on compressive sensing

In this paper, we propose a novel compressive sensing (CS) based approach for sparse target counting and positioning in wireless sensor networks. While this is not the first work on applying CS to count and localize targets, it is the first to rigorously justify the validity of the problem formulation. Moreover, we propose a novel greedy matching pursuit algorithm (GMP) that complements the well-known signal recovery algorithms in CS theory and prove that GMP can accurately recover a sparse signal with a high probability. We also propose a framework for counting and positioning targets from multiple categories, a novel problem that has never been addressed before. Finally, we perform a comprehensive set of simulations whose results demonstrate the superiority of our approach over the existing CS and non-CS based techniques.

Superimposed code based channel assignment in multi-radio multi-channel wireless mesh networks

Motivated by the observation that channel assignment for multiradio multi-channel mesh networks should support both unicast and local broadcast1, should be interference-aware, and should result in low overall switching delay, high throughput, and low overhead, we propose two flexible localized channel assignment algorithms based on s-disjunct superimposed codes. These algorithms support the local broadcast and unicast effectively, and achieve interference-free channel assignment under certain conditions. In addition, under the primary interference constraints2, the channel assignment algorithm for unicast can achieve 100% throughput with a simple scheduling algorithm such as the maximal weight independent set scheduling, and can completely avoid hidden/exposed terminal problems under certain conditions. Our algorithms make no assumptions on the underlying network and therefore are applicable to a wide range of MR-MC mesh network settings. We conduct extensive theoretical performance analysis to verify our design.

An introduction to type-2 TSK fuzzy logic systems

Type-2 fuzzy sets allow one to handle linguistic uncertainties. This paper presents the architectures of three type-2 TSK fuzzy logic systems (FLSs). There are three because of the possible type-1 or type-2 natures of the antecedent memberships and parameters of the consequent. The output of a type-2 TSK FLS is a type-1 set (which can be defuzzified), whereas the output of a type-1 TSK FLS is a crisp number (type-0 set). Simulation results are provided that compare the outputs of type-2 and type-1 TSK FLSs.

Ad hoc wireless network traffic-self-similarity and forecasting

Lots of works have been carried out to discuss the self-similarity of Ethernet and World Wide Web traffic. In this letter, we study the ad hoc wireless network traffic collected in an ad hoc network (AHN) testbed and show that the ad hoc wireless network traffic is self-similar, which validates that AHN traffic is forecastable because self-similar time-series can be forecasted. We apply a fuzzy logic system to ad hoc wireless network traffic forecasting and simulation results show that it performs much better than does an LMS adaptive filter. All these studies are very important for evaluating network capacity and determining the battery power mode based on the forecasted traffic workload.

Overcoming time-varying co-channel interference using type-2 fuzzy adaptive filters

This paper presents a method for overcoming time-varying co-channel interference (CCI) using type-2 fuzzy adaptive filters (FAF). The type-2 FAF is realized using an unnormalized type-2 Takagi-Sugeno-Kang fuzzy logic system. A clustering method is used to adaptively design the parameters of the FAF. We use transversal equalizer and decision feedback equalizer structures to eliminate the CCI. Simulation results show that the equalizers based on type-2 FAFs perform better than the nearest neighbor classifiers or the equalizers based on type-1 FAFs when the number of co-channels is much large than 1.