Despite extensive research on mobility planning, a situation in which multiple travellers participate in a cooperative endeavor to help each other optimize their objectives has not been investigated. Furthermore, due to the inherent multi-participant nature of the mobility problem, the existing solutions fail to produce ground-truth optimal mobility plans in the practical sense despite their claimed and well-proven theoretical optimality. This paper presents a team mobility trip-planning solution. The solution comes in the form of a game theoretic treatment of the planning problem. The solution allows multiple participants to engage in a collective solution-finding endeavor. The game theoretic solution utilizes the bargaining game to allow for the drivers to negotiate using their chosen strategies. This paper provides mathematical formulations of the team-planning problem, the developed solution, and the bargaining game. Furthermore, various aspects of the described game, such as game solution existence and game balancedness, are investigated. Simulation results are reported to demonstrate the efficiency of the developed game theoretic formulation under various scenarios.

Recent years have witnessed a surge of new methods by which commuters are accessing transportation modals. The use of social taxi networks is one of the most promising door-to-door transportation methods. In social taxi networks, commuters use their smart devices to contact social taxi service providers based on their geographical proximity and the listed fare prices. These taxis can be traditional taxis or privately owned vehicles. However, with the success of this new car hailing mechanism, there is an emergence of a number of problems. One of these problems is the territory allocation problem. i.e., the majority of social taxi drivers choose areas in which the most likely number of customers will be present. Therefore, they negatively impact each other's profit by offering a high supply of services. This paper develops a cooperative territory allocation approach such that, through negotiation between service providers, can reduce the conflict between taxi drivers. Game theory is used to formulate the territory sharing problem, which can be solved using bargaining-based solution model. The solution model is designed to correspond to a no regret game for which the outcome corresponds to a coarse correlated equilibrium. Simulation work results are provided to support the findings in this paper.

Utilizing vehicles for networking purposes has always been a challenge. This is mainly due to the minimum density of connected-vehicles required. The locations of these vehicles should be shareable and reasonably predictable for efficient position-based routing protocols to be implemented. Their Vehicle-to-Vehicle (V2V) communication cooperation should be well-incentivized for efficient networking to be realized. Regular vehicles struggle to have all of these properties. Public transportation vehicles, on the other hand, are well-positioned in this regard; their number is proportional to the number of city residents while being uniformly distributed throughout the day, their locations have no privacy concerns while being highly predictable and their V2V communication cooperation is easily enforceable by the single administration authority they usually have. With efficient networking, public transportation vehicles can become the reliable communication backbone for other vehicle categories. In order to investigate their networking potential, we present for the firs time, in this paper, a data analysis study of realistic public transportation mobility datasets representing the Grand River Transit bus service offered throughout the Region of Waterloo, Ontario, Canada. We show both the data preprocessing and processing phases. The processing phase is mainly based on discovering bus groups using hierarchical clustering. This is done while varying the minimum degree of intra-cluster connectivity and the maximum intra-cluster communication range. Based on this data analysis approach, we show the promising networking potential of public transportation vehicles and provide design guidelines for future networking solutions utilizing them.

Power and computing limitations hinder the ability of many devices to support stringent security protocols. Smart sensors, RFID tags, and wearable devices are typical examples of such devices. Lightweight cryptography is concerned with the design and implementation of cryptography algorithms in environments with limited computing and power resources. This paper presents a realization of a hardware efficient lightweight cryptography block cipher SIMON in C/C++ (SIMON 32/64 and 64/128). Analysis is performed in order to investigate its input/output cross correlation and among output sets. The proposed block cipher's immunity to linear span attacks is also investigated using the Berlekamp-Massy algorithm. It is concluded that the proposed block cipher is not immune to linear span attacks, as the analysis has shown a linear span for certain components to be less than N/2, with a profile of probability of 1/3 in 1 million iterations.

With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. MRI scans are shown to be the most effective strategy to enable early diagnosis of dementia. Nevertheless, the early diagnosis of dementia is a challenging task due to the high dimensionality of MRI scans that may degrade the effectiveness of machine learning models. Feature abstraction is a part of dimensionality reduction process need by researchers to represent input data in its simplest form that results in a more-robust system model. It is a technique that collects relevant features and ignores irrelevant or redundant ones from data without loss of much key information. This paper proposes a novel feature abstraction method using sparse autoencoder (SAE) to reduce the dimensionality of and extract key features from MRI neuroimages. These features and that obtained from the popular PCA approach are then used to train a Linear Discriminant Analysis (LDA) and Logistic Regression classifiers in order to compare their prediction accuracy. The experimental results show that the proposed approach yields higher classification accuracy compared to that using the PCA by 8 percent of classification accuracy. The experimental results show that the use of features learned by SAE in early diagnosis of multi-type dementia provides better classification performance than the use of raw image pixel intensities for diagnosing dementia.

Cooperative spectrum sensing is a powerful sensing approach which is based on sharing information about channel activities among secondary users (SUs). Cooperative spectrum sensing aims to overcome hidden node problem, shadowing and fading problems, it also enhances sensing accuracy. However, sensing accuracy may degrade due to various reasons: if environmental properties are poor or intra-node characteristics are continuously altering. Thus, this paper proposes a novel approach of a multi-stage hybrid cooperative spectrum sensing model. The first stage, integrates a fuzzy logic system for local fusion center, whereby SU-mobility and its environmental properties, and its neighbors' environmental properties are included in local sensing decision process. Second stage proposes a neural network, based on backpropagation learning algorithm, for global fusion center. All SUs transmit their sensing information, local decision, and their intra-node characteristics to be augmented for an optimized global sensing decision. Neural network is trained based on a real-world measured power dataset. Extensive simulations on the proposed multi-stage model were performed. The results showed high robustness against instantaneous changes in SU-mobility levels and good detection performance at very low signal-to-noise ratio (SNR) levels. The proposed prediction model outperformed the state-of-the-art work with high detection accuracy at very poor environmental conditions and at different speeds levels.

This paper presents a low-cost real-time lane-determination system that fuses micro-electromechanical systems inertial sensors (accelerometers and gyroscopes), global navigation satellite system (GNSS), and commercially available road network maps. The system can be used for intelligent transportation systems, telematics applications, and autonomous driving. The system does not depend on visual markings or highly precise GNSS technology, such as DGPS or RTK, and it does not need explicit lane-level resolution maps. High-resolution estimation of the vehicle's position, velocity, and orientation is implemented by fusing inertial sensors with GNSS in a loosely coupled mode using extended Kalman filter. A curve-to-curve road-level map-matching is implemented using a hidden Markov model followed by a least-square regression step that estimates the vehicle's lane. The system includes a lane-change detector based on inertial sensors and the filtered vehicle's state. The system has been realized in real time and tested extensively on real-road data. Experiments showed robust map-matching in challenging road intersections and a 97.14% lane-determination success rate.

Modern transportation systems suffer from a variety of problems. The most notable among these problems is traffic congestions. Traffic congestions have been a subject for much of research discussing the impact of the traffic on the environment, the financial cost it incurs, and the safety risks it poses. The research of this paper alternatively suggests a solution model to mitigate the congestion problem: the personalization of the trip planning process based on the social habits of the travellers. These habits can be derived from monitoring daily activities, calendar events, and social feeds from the various outlets of social media. The different sentiments of the travellers can be processed to generate several planning profiles. Travellers will be able to choose the profiles representing their personalities and subject them to further customization. The customized profile can be considered as an avatar. As the avatar assumes the traveller's planning persona, it will start to automatically plan the daily trips on behalf of the traveller. To fully grasp the functionality of the proposed approach, Game theory is used to analyze the new trip planning approach and its effect on the traffic flow.

With limited battery supply, power is a scarce commodity in wireless sensor networks. Thus, to prolong the lifetime of the network, it is imperative that the sensor resources are managed effectively. This task is particularly challenging in heterogeneous sensor networks for which decisions and compromises regarding sensing strategies are to be made under time and resource constraints. In such networks, a sensor has to reason about its current state to take actions that are deemed appropriate with respect to its mission, its energy reserve, and the survivability of the overall network. Sensor Management controls and coordinates the use of the sensory suites in a manner that maximizes the success rate of the system in achieving its missions. This article focuses on formulating and developing an autonomous energy-aware sensor management system that strives to achieve network objectives while maximizing its lifetime. A team-theoretic formulation based on the Belief-Desire-Intention (BDI) model and the Joint Intention theory is proposed as a mechanism for effective and energy-aware collaborative decision-making. The proposed system models the collective behavior of the sensor nodes using the Joint Intention theory to enhance sensors’ collaboration and success rate. Moreover, the BDI modeling of the sensor operation and reasoning allows a sensor node to adapt to the environment dynamics, situation-criticality level, and availability of its own resources. The simulation scenario selected in this work is the surveillance of the Waterloo International Airport. Various experiments are conducted to investigate the effect of varying the network size, number of threats, threat agility, environment dynamism, as well as tracking quality and energy consumption, on the performance of the proposed system. The experimental results demonstrate the merits of the proposed approach compared to the state-of-the-art centralized approach adapted from Atia et al. [2011] and the localized approach in Hilal and Basir [2015] in terms of energy consumption, adaptability, and network lifetime. The results show that the proposed approach has 12 × less energy consumption than that of the popular centralized approach.

Trip planning is a well-cited problem that has been typically addressed, to a large extent, as a shortest distance path-finding problem that can be extended to reflect temporal objectives and/or constraints. This paper takes an alternative perspective to the trip-planning problem in the sense of it being a behavioral driven problem, thus allowing for multitudes of traveler centric objectives and constraints, as well as aspects of the environment, as they pertain to the traveler preferences to be incorporated in the process. This paper introduces a Behavior Driven Trip Planner (BDTP) to operate as an in-vehicle guidance system. The BDTP is designed as a modular system that combines linguistic road assessment with traveler-centric decision-making. This paper introduces traveler's doctrines, a concept that synthesizes the traveler's specific demands. Hard attributes/objectives, such as time windows and trip monetary allowances, are enforced in the process of determining the final decision about the trip. This paper introduces the underlying mathematical formulation for the developed system and explains how the formulation works to achieve the optimal performance. Simulation results are presented to demonstrate the efficacy of the system to capture and respond to traveler preferences under a wide range of scenarios.

This paper provides a description to a class of cooperatives bargaining games for the ride-souring applications. We first describe the ride-sourcing problem, then we provide a solution formulation for this problem. Finally, a bargaining based solution model is presented.

Modern smartphones are globally ubiquitous. As such, an increasing number of drivers have their smartphone in their vehicle while driving. These phones are equipped with powerful sensing, processing, and communication capabilities. This provides an opportunity to deploy smartphones in modern telematics and mobile telemetry technologies to enable the collection of driving data. Such data can be exploited to obtain insights regarding the vehicle driving patterns as well as the drivers' skills. These insights are valuable in many applications including the usage-based insurance, young driver coaching, and fleet management solutions. However, the sensory data provided by a smartphone must be reoriented with respect to the vehicle to be utilized in such applications. This requires the orientation of the smartphone relative to the vehicle reference system to be estimated through a calibration process. Furthermore, the orientation of a smartphone can vary at any time during a trip due to extraneous factors such as user interaction. This makes the orientation calibration process a challenging task. This paper describes an opportunistic calibration method that continuously monitors a smartphone orientation and compensates for its variation, as necessary. The proposed method relies on the probabilistic fusion of built-in sensors; in particular, the GPS, accelerometer, gyroscope, and magnetometer. The extensive experiments conducted using real-world driving data illustrate the effectiveness of the proposed opportunistic calibration method.

The ability of sensors to self-organize is an important asset in surveillance sensor networks. Self-organize implies self-control at the sensor level and coordination at the network level. Biologically inspired approaches have recently gained significant attention as a tool to address the issue of sensor control and coordination in sensor networks. These approaches are exemplified by the two well-known algorithms, namely, the Flocking algorithm and the Anti-Flocking algorithm. Generally speaking, although these two biologically inspired algorithms have demonstrated promising performance, they expose deficiencies when it comes to their ability to maintain simultaneous robust dynamic area coverage and target coverage. These two coverage performance objectives are inherently conflicting. This paper presents Semi-Flocking, a biologically inspired algorithm that benefits from key characteristics of both the Flocking and Anti-Flocking algorithms. The Semi-Flocking algorithm approaches the problem by assigning a small flock of sensors to each target, while at the same time leaving some sensors free to explore the environment. This allows the algorithm to strike balance between robust area coverage and target coverage. Such balance is facilitated via flock-sensor coordination. The performance of the proposed Semi-Flocking algorithm is examined and compared with other two flocking-based algorithms once using randomly moving targets and once using a standard walking pedestrian dataset. The results of both experiments show that the Semi-Flocking algorithm outperforms both the Flocking algorithm and the Anti-Flocking algorithm with respect to the area of coverage and the target coverage objectives. Furthermore, the results show that the proposed algorithm demonstrates shorter target detection time and fewer undetected targets than the other two flocking-based algorithms.

The development of a new drug largely depends on trial and error. It typically involves synthesizing thousands of compounds that finally becomes a drug. This process is extremely expensive and slow. Therefore, the ability to accurately predict the biological activity of molecules, and understand the rationale behind those predictions would be of great value to the pharmaceutical industry. Gradient Boosting Machines (GBMs) are powerful ensemble learning techniques that have been successfully applied to several low-dimensional applications. Despite their high accuracy, GBMs suffer from major drawbacks such as high memory-consumption. In this paper, using real, high-dimensional (i.e. 1776 predictors) molecules dataset, we demonstrate that by using different feature selection/reduction techniques, the computations costs for building and tuning GBMs can be substantially reduced at a slight drop in prediction accuracy. In addition, by fusing the decisions made by the ensembles using two fusion techniques, namely a majority vote and an optimized feedforward neural network, we obtain a better prediction accuracy than the individual accuracy of all ensembles.

Cellular Network Localization (CNL) is a vital source of information to a wide range of emerging applications, to name a few, Location based services, traffic analysis and sensing, vehicles' anti-theft systems. Developing, validating, and testing of such applications require massive data sets of CNL - a prerequisite that is impractical to be collected. Furthermore, it can not be simulated using hard computing models due to the lack of estimators that can ascribe accurate level of localization accuracy as to the change in various spatial properties. In this paper, we propose a soft computing model to simulate the CNL performance, called SCMCL. This model carries out the estimation of CNL performance according to a knowledge base built on the basis of empirical findings reported in the literature. SCMCL is fed with real-life vehicular traces and produces CNL. SCMCL is validated and demonstrated throughout various operation conditions.

This paper presents a method for designing binary trees for SVM classification. The proposed algorithm, multi-modal binary tree (MBT) tolerates misclassification in the upper nodes of the tree, allowing points to be classified in either output regardless of the initial specified class groupings. MBT can separate classes that are inseparable with a single classifier by using a piecewise division. The algorithm also incorporates feature selection for the individual classifiers in the system. Classification results on several artificial and real data sets show that the proposed algorithm performs well compared to existing methods for multi-class SVM classification, and although the classifiers are larger, the time required to classify a point is smaller.

Feature selection can decrease classifier size and improve accuracy by removing noisy and/or redundant features. However, it is possible for feature selection to yield features that are only partially informative about the classes in the set. These features are beneficial for distinguishing between some classes but not others. In these cases, it is beneficial to divide the large classification problem into a set of smaller problems, where a more specific set of features can be used to classify different classes. Dividing a problem this way is also common when the base classifier is binary, and the problem needs to be reformulated as a set of two-class problems so it can be handled by the classifier. This paper presents a method for multiclass classification that simultaneously formulates a binary tree of simpler classification subproblems and performs feature selection for the individual classifiers. The feature selected hierarchical classifier (FSHC) is tested against several well-known techniques for multiclass division. Tests are run on nine different real data sets and one artificial data set using a support vector machine (SVM) classifier. The results show that the accuracy obtained by the FSHC is comparable with other common multiclass SVM methods. Furthermore, the results demonstrate that the algorithm creates solutions with fewer classifiers, fewer features, and a shorter testing time than the other SVM multiclass extensions.

Global Positioning System (GPS) localization has been attracting attention recently in various areas, including intelligent transportation systems (ITSs), navigation systems, road tolling, smart parking, and collision avoidance. Although, various approaches for improving localization accuracy have been reported in the literature, there is still a need for more efficient and more effective measures that can ascribe some level of accuracy to the localization process. These measures will enable localization systems to manage the localization process and resources to achieve the highest accuracy possible and to mitigate the impact of inadequate accuracy on the target application. The localization accuracy of any GPS system depends heavily on both the technique it uses to compute locations and the measurement conditions in its surroundings. However, while localization techniques have recently started to demonstrate significant improvement in localization performance, they continue to be severely impacted by the measurement conditions in their environment. Indeed, the impact of the measurement conditions on the localization accuracy in itself is an ill-conditioned problem due to the incongruent nature of the measurement process. This paper proposes a scheme to address localization accuracy estimation. This scheme involves two steps, namely, measurement condition disambiguation and enhanced location accuracy classification. Real-life comparative experiments are presented to demonstrate the efficacy of the proposed scheme in classifying GPS localization accuracy under various measurement conditions.

Practitioners in the area of neurology often need to retrieve multimodal magnetic resonance (MR) images of the brain to study disease progression and to correlate observations across multiple subjects. In this paper, a novel technique for retrieving 2-D MR images (slices) in 3-D brain volumes is proposed. Given a 2-D MR query slice, the technique identifies the 3-D volume among multiple subjects in the database, associates the query slice with a specific region of the brain, and retrieves the matching slice within this region in the identified volumes. The proposed technique is capable of retrieving an image in multimodal and noisy scenarios. In this study, support vector machines (SVM) are used for identifying 3-D MR volume and for performing semantic classification of the human brain into various semantic regions. In order to achieve reliable image retrieval performance in the presence of misalignments, an image registration-based retrieval framework is developed. The proposed retrieval technique is tested on various modalities. The test results reveal superior robustness performance with respect to accuracy, speed, and multimodality.

This work presents a method for improving classifier accuracy through joint feature selection and hierarchical classifier design with genetic algorithms. The hierarchical classifier divides the classification problem into a set of smaller problems using multiple feature-selected classifiers in a tree configuration to separate the data into progressively smaller groups of classes. This allows the use of more specific feature sets for each set of classes. Several existing performance measures for evaluating the feature sets are investigated, and a new measure, count-based RELIEF is proposed. The joint feature selection and hierarchical classifier design method is tested on two artificial data sets. Results indicate that the feature selected hierarchical classifiers are able to achieve better accuracy than a non-hierarchical classifier using feature selection alone. The newly proposed performance measure is also tested and shown to provide a better indication of classifier performance than existing methods.

Location based services in wireless sensor networks are quite demanding applications especially in indoors, such that accurate localization of objects and people in indoor environments has long been considered as one of important building blocks in wireless systems. In this paper, we investigate sensor location estimation problem where a target sensor measures inconsistent signals as received-signal-strength or time-of-arrival from anchor sensors with known locations, whereas target sensor location must be estimated. We know that even in large scale wireless sensor networks, information are relatively sparse compared with the number of sensors. In such networks, the localization problem can be recast as a sparse signal recovery problem in the discrete spatial domain from a small number of linear measurements by solving an under-determined linear system. By exploiting the compressive sensing theory, sparse signals can be recovered from far fewer samples than Nyquist sampling rate. Our approach uses a few number of inconsistent measurements to find the wireless device location over a non-symmetric spatial grid. In this method, an ℓ1-norm minimization program is used to recover the wireless user location. The performance of the proposed method is evaluated through simulations with synthetic and real measurements.

An efficient medium access control (MAC) protocol should yield maximum throughput and fairness. However, because these two performance metrics have conflicting interests, an effective solution must address the trade-off between them. In this paper, we study the performance of the IEEE 802.11p MAC protocol in vehicle-to-infrastructure communications. The main focus is the trade-off between the system throughput and the level of fairness among the communicating nodes, which leads to a customized optimization problem. Accordingly, we formulate a multiobjective optimization problem to optimize both throughput and fairness, and propose a dynamic mechanism to maintain fairness among the mobile nodes. The optimization problem is solved by means of an approximate numerical solution, the results of which demonstrate the effectiveness of the proposed MAC scheme in terms of throughput and short-/long-term fairness.

Efficiency of transportation systems is defined as relationship between costs and benefits. Congestion is a phenomena that increase utilization cost in different modes of transportation including the road networks. In this paper, a kernel-based density estimation method is utilized to extract the congestion spots in road networks based on collected position samples with time-stamp from floating car data. A probabilistic framework is developed to find optimized weights of kernels in an approximation function, centered at points-of-interest by minimizing the Cramer-von Mises distance between localized cumulative distributions of mixture of Dirac distributions of position samples and Gaussian mixtures of points-of-interest in a pre-defined time window. The approximation density function by optimized kernels' weights can be used to estimate the mobile vehicles density in a specific time and space. The proposed method can be significantly improved if we have a spatial-temporal model of floating car data.

Vehicle localization is a key issue that has recently attracted attention in a wide range of applications. Navigation, vehicle tracking, emergency calling, and location-based services are examples of emerging applications with a great demand for location information. The Global Positioning System (GPS) has been the de facto standard solution for the vehicle-localization problem. Nevertheless, GPS-based localization is inaccurate and unreliable due to GPS' inherent poor performance in vertical positioning and the prevalent horizontal movement, in addition to anomalies caused by line-of-sight occlusions and multipath issues in urban canyons. Although augmenting GPS localization with inertial sensory data has demonstrated significant performance improvements, there remain situations that give rise to degraded localization accuracy-a deficiency that many applications cannot tolerate. In this paper, we propose intervehicle-communication-assisted localization, a localization technique that takes advantage of the emerging vehicle ad hoc networks environments. Communication among vehicles is utilized to compute a relative vehicle location, the integration of which with motion information and GPS location estimates leading to highly accurate vehicle localization. This proposed localization technique is tested in various simulated road-segment scenarios. It is evident from the simulation results that intervehicle communication has the potential to lead to the improvement of the robustness and accuracy of vehicle-location estimation.

As progress in VANETs research continues, there is a persuasive need to support Quality of Service (QoS) routing in such networks. While greedy forwarding is used in many MANETs applications, it is found that it is not convenient for VANETs applications. In this paper, we investigate the important and difficult challenge of QoS routing in VANETs. First, we present an adaptive message routing protocol that uses up to date information about the local topology in order to find the route with minimum end-to-end delay while maintaining a threshold for the connectivity probability and hop count. Then, we propose a genetic algorithm to solve this. To do so, we formulate the QoS routing as a constrained optimization problem. We also derive analytical expressions for the delay as well as the connectivity probability of a route in a two-way street scenario. Numerical and simulation results show that our algorithm gives an optimal or near optimal solutions, which provides an interactive and effective design environment and enriches our protocol performance compared to GPCR.

Efficient location management is one of the major challenges in vehicular ad hoc networks (VANETs). Due to the high mobility of vehicles and the increase in their number, the location information updating and querying messages will consume the limited bandwidth of VANETs. This involves the development of a scalable and locality-aware location service management protocol. In this paper, we propose a promising solution called the modified region-based location service management protocol (MRLSMP), which utilizes the existing infrastructure on the road as a location management service entity. To evaluate the efficiency of our proposal, we compare our scheme with existing solutions using both analytical and simulation approaches. Specifically, we develop analytical models to evaluate the total control overhead. Numerical and simulation results show that our protocol scales better than existing schemes, when increasing the size of VANETs which enhances the feasibility of such large scale ad hoc networks.

Mobile Ad hoc networks (MANETs) are gaining increased interest as the technology that potentially will make mobile computing a tangible reality. The self-control, selforganization, topology dynamism, and bandwidth limitation of the wireless communication channel make MANETs' implementation a challenging task. The Connected Dominating Set (CDS), a.k.a. virtual backbone or spine, has been proposed to facilitate routing, broadcasting, and establishing a dynamic infrastructure for distributed location databases in MANETs. Minimizing the CDS cardinality simplifies the MANET's abstracted topology and allows for using shorter routes. Since finding the minimum size CDS (MCDS) is NP-complete, approximation algorithms and heuristics are used to tackle this problem. It has been reported that localized CDS creation algorithms run fast and generate light signaling overhead; examples of these algorithms are Wu and Li algorithm and its Stojmenovic variant, the MPR algorithm, and Alzoubi algorithm. Some theoretical performance analysis of these algorithms is presented in the literature; however, this analysis has not been validated across any physical or at least simulation-based measures. Moreover, the presented theoretical performance analysis has overlooked the cost of maintaining the CDS in the presence of topology changes. In this paper, a simulation-based evaluation of the performance of these algorithms is conducted using the ns-2 network simulator. Moreover, the effects of mobility rates and network size on the performance of each algorithm is investigated.

Region-based descriptors often use moments to describe shapes. The discreet radial Tchebichef moment descriptors have been proposed. The radial Tchebichef moments are invariant with respect to image rotation. In order to achieve the scale invariance, researchers resort to resizing the original shape to predetermined size. This traditional scheme of scaling is time expensive and leads to the loss of some characteristics of a shape. Therefore, moments derived using the traditional normalization scheme may differ from the true moments of the original shape. In this paper, a simple yet powerful scheme has been proposed to derive a new set of scale invariants of radial Tchebichef moments. This scheme uses the area and the maximum radial distance of a shape to normalize the radial Tchebichef moments. The MPEF-7 scale-invariant database is used to evaluate the performance of the proposed scheme against four commonly used shape descriptors.

This paper proposes Constrained Rationality, a formal qualitative goals and constraints reasoning framework for single and multi agents to analyze and rationalize about strategic decisions/conflicts. The framework suggests bringing back the strategic decision making problem to its roots: reasoning about options/alternatives, not to satisfy a set of preferences, but rather to satisfy the explicitly stated strategic and conflicting goals an agent has, given the internal and external complex and conflicting realities/constraints the agent has. The paper analyzes the relations among goals and constraints, how value property labels for goals (such as their achievement or prevention levels) propagates through these relations, and proposes a set of propagation rules and an algorithm to calculate the final value labels for goals at any point of time. The paper also presents some preliminary experimental results on using the algorithm to reason about a business strategic decision making scenario.

Mobile ad hoc networks (MANETs) are gaining increased interest as the technology that potentially will make the nowadays illusion of mobile computing a tangible reality. vehicular ad-hoc networks (VANETs) are the special kind of MANETs that aims at providing communications among vehicles on the roads. The connected dominating set (CDS), a.k.a. virtual backbone or spine, has been proposed to facilitate routing, broadcasting, and establishing a dynamic infrastructure for distributed location databases in VANETs. Minimizing the CDS cardinality simplifies the VANETpsilas abstracted topology and allows for using shorter routes. Since, it is NP-complete to find the minimum size CDS (MCDS), approximation algorithms and heuristics are used to tackle this problem. It has been reported that localized CDS creation algorithms run fast and generate light signaling overhead. Some theoretical performance analysis of these algorithms is presented in the literature; however, this analysis has not been validated across any physical or at least simulation-based measures. Moreover, the cost of maintaining the CDS in the presence of topology changes is an important cost that is overlooked most of the time. In this paper, a simulation-based comparison between the performance of these algorithms is conducted using the ns2 network simulator. Moreover, the effect of mobility rate and network size on the performance of each algorithm is investigated.

One of the major challenges for vehicular ad hoc networks (VANETs) is related to efficient location management issue. In this paper, we propose a new region-based location service management protocol (RLSMP) that uses mobility patterns as means to synthesize vehicle movement and thus can be used in VANETs applications. One of the key distinguishing features of our solution from existing literature is its scalability since it uses message aggregation in both updating and querying, and promises locality awareness as well as minimum signaling overhead. To evaluate the efficiency of our proposal, we compare our scheme with existing solutions using both analytical and simulation approaches. To achieve this, we develop analytical models to evaluate the location updates cost. Numerical and simulation results show that our protocol scales better than existing schemes, when increasing the size of VANET which enhances the feasibility of such large scale ad hoc networks.

For intelligent sensory systems, it is highly desirable to develop assessment methods that can continuously evaluate the reliability of potential sensory strategies taking into consideration changes in observation conditions. This relies on measuring a set of complementary features from multiple sensors and combining these features in an "intelligent" way that maximizes information gather and minimizes the impact of noise coming from the individual sensors. In this work, we formulate a statistical assessment method for estimating the reliability of observation conditions and propose an optimal mapping into weighting measures using genetic algorithms. Our approach is particularly beneficial for multimodal systems such as audio-visual speech recognition (AVSR).

Shape descriptors have demonstrated encouraging potential in retrieving images based on image content. A number of shape descriptors have been reported in the literature. Nevertheless, most of the reported descriptors still face accuracy and computational challenges. Fourier descriptors are considered to be promising descriptors as they are based on sound theoretical foundation, and possess computational efficiency and attractive invariance properties. In this paper, we propose a novel Fourier descriptor based on contour curvature. The proposed descriptor takes an unconventional view of the curvature-scale-space representation of a shape contour as it treats it as a 2-D binary image (hence referred to as Curvature-Scale Image, or CSI). The invariant descriptor is derived from the 2-D Fourier transform of the Curvature-Scale Image. This allows the descriptor to capture detailed dynamics of the shape curvature and enhance the efficiency of the shape matching process. Experiments using images from the MPEG-7 database have been conducted to compare the performance of the proposed descriptor with the Curvature- Scale-Space Descriptor (CSSD), the Generic Fourier Descriptor (GFD), and the 1-D Fourier Descriptor (1-FD). The proposed descriptor demonstrated superior performance.

Awareness of a vehicle's precise location in VANET is vital so that any vehicle can provide accurate data to its peers. Currently, typical localization techniques integrate the GPS receiver data and the measurements of the vehicle's motion. However, when the vehicle passes through an environment that creates multipath signals, these techniques fail to produce the high localization accuracy that they attain in open environments. The goal of this research is to minimize the multipath effect with respect to the localization accuracy of the vehicles in VANET. The proposed technique, IVCAL, takes advantage of the communications among the VANET vehicles in order to obtain more information from the vehicle's neighbours. The proposed technique integrates all these pieces of information with the vehicle's own data and applies optimization techniques to minimize the error in the location estimate. The simulation results in this paper show a decrease of up to 53% in the location estimate error compared to the error in the traditional techniques.

In this paper, a framework is proposed for the distributed control and coordination of multiagent systems (MASs). In the proposed framework, the control of MASs is regarded as achieving decentralized control and coordination of agents. Each agent is modeled as a coordinated hybrid agent, which is composed of an intelligent coordination layer and a hybrid control layer. The intelligent coordination layer takes the coordination input, plant input, and workspace input. In the proposed framework, we describe the coordination mechanism in a domain-independent way, i.e., as simple abstract primitives in a coordination rule base for certain dependence relationships between the activities of different agents. The intelligent coordination layer deals with the planning, coordination, decision making, and computation of the agent. The hybrid control layer of the proposed framework takes the output of the intelligent coordination layer and generates discrete and continuous control signals to control the overall process. To verify the feasibility of the proposed framework, experiments for both heterogeneous and homogeneous MASs are implemented. The proposed framework is applied to a multicrane system, a multiple robot system, and a MAS consisting of an overhead crane, a mobile robot, and a robot manipulator. It is demonstrated that the proposed framework can model the three MASs. The agents in these systems are able to cooperate and coordinate to achieve a global goal. In addition, the stability of systems modeled using the proposed framework is also analyzed.

Shape-based image retrieval techniques are among the most successful content-based image retrieval (CBIR) approaches. In recent years, the number of shape-based image retrieval techniques has dramatically increased; however, each technique has both advantages and shortcomings. This paper proposes a consensus-based fusion algorithm to integrate several shape-based image retrieval techniques so as to enhance the performance of the image retrieval process. In this algorithm, several techniques work as a team: they exchange their ranking information based on pair-wise co-ranking to reach a consensus that will improve their final ranking decisions. Although the proposed algorithm handles any number of CBIR techniques, only three common techniques are used to demonstrate its effectiveness. Several experiments were conducted on the widely used MPEG-7 database. The results indicate that the proposed fusion algorithm significantly improves the retrieval process.

Shape-based image description is an important approach to content-based image retrieval (CBIR). A variety of techniques are reported in the literature that aim to represent objects based on their shapes; each of these techniques has its advantages and disadvantages. Fourier descriptor (FD), a simple yet powerful technique, has attractive properties such as rotational, scale, and translational invariance. In this paper we investigate this technique and present a novel shape registration method for extracting Fourier descriptors. When evaluated against curvature scale space (CSS) and Zernike moments (ZM) in shape-based image retrieval, the proposed technique exhibits superior performance.

The purpose of this work is to enhance the performance of visual speech recognition by using independent component analysis (ICA) in order to extract statistically independent visual features. In the first place, we derive the optical flow fields for consecutive frames of people speaking. Then, we use ICA in order to derive the basis images for these optical flow fields. The coefficients of these basis flow fields will comprise the visual features of interest. We will show that using ICA on optical flow fields yields better classification results than the traditional approaches based on principal component analysis (PCA) for instance. Our approach is evaluated for the Tulipsi database and compared to the standard approaches.

Modern statistical techniques used in the field of natural language processing are limited in their applications by the fact they suffer from the loss of most of the semantic information contained in text documents. Fuzzy techniques have been proposed as a way to correct this problem through the modelling of the relationships between words while accommodating the ambiguities of natural languages. However, these techniques are currently either restricted to modelling the effects of simple words or are specialized in a single domain. In this paper, we propose a novel statistical-fuzzy methodology to represent the actions described in a variety of text documents by modelling the relationships between subject-verb-object triplets. The research will focus in the first place on the technique used to accurately extract the triplets from the text, on the necessary equations to compute the statistics of the subject-verb and verb-object pairs, and on the formulas needed to interpolate the fuzzy membership functions from these statistics and on those needed to de fuzzify the membership value of unseen triplets. Taken together, these sets of equations constitute a comprehensive system that allows the quantification and evaluation of the meaning of text documents, while being general enough to be applied to any domain. In the second phase, this paper will proceed to experimentally demonstrate the validity of our new methodology by applying it to the implementation of a fuzzy classifier conceived especially for this research. This classifier is trained using a section of the Brown Corpus, and its efficiency is tested with a corpus of 20 unseen documents drawn from three different domains. The positive results obtained from these experimental tests confirm the soundness of our new approach and show that it is a promising avenue of research.

One of the many issues that confront traditional statistical approaches of natural language understanding (NLU) is on how to overcome the insufficient co-occurrence information caused by the limited boundary of statistical approaches. Researches have long used the imparting of human knowledge into statistical approaches, including definition of rules and collections of hierarchy of concepts. However, these are difficult to define even for a domain expert. They are also very much people and domain dependent. This study proposes a fuzzy approach to tackle these issues in a way as to provide a methodology for logical reorganizing context in order to tackle the issue of boundary limitation, to create the more reasonable and understandable word association which will be referenced as membership degree in latter stage, and to make the processes of imparting of human knowledge easier and less domain dependent. The accomplishment of these tasks could be achieved through the concept of precisiated natural language (PNL)

The coordinated hybrid agent (CHA) framework for the control of multi-agent systems (MASs) has been used to model both the homogeneous and the heterogeneous multi-agent systems. In this framework, the control of the MASs is regarded as a decentralized control and coordination of agents. The CHA framework is able to implement coordination tasks for multi-agent systems. In this study, the optimization of MASs modelled by the CHA framework is studied. The time-driven dynamics and the event-driven dynamics for the optimization of a CHA system are given. The optimization problem of the MASs is analyzed. An example is also given to illustrate how to define the optimization problem for a CHA. The forward algorithm is also introduced for solving the optimization problem for a CHA system.

Humans are immersed in a high-tech computing environment. Dependence on the pervasiveness of modern devices encompasses even simple everyday tasks. Unfortunately, the rapidly increasing expectations on the intelligence of these devices often exceeds their abilities. Devices are expected to perceive their environment, understand our intent, and autonomously carry out appropriate tasks. This paper presents an approach to bring device perception one step closer to meeting the high user expectations. A novel application of fuzzy hidden Markov models to automatically identify dynamic facial expressions is proposed in a human-device interaction context. A low-complexity vision based facial feature tracker is integrated with a hidden Markov model adapted to take advantage of fuzzy measures. Benefits over traditional hidden Markov mode Is for facial expression recognition include a reduction in training time, and improved flexibility for use as a valuable part of a larger multimodal system for natural human-device interaction

This paper presents a data fusion approach for pattern classification, based on the fuzzy evidential reasoning technique. First, a new fuzzy evidence structure model is introduced to formulate probabilistic evidence and fuzzy evidence in a unified manner. A generalized Dempster's rule is then used to combine fuzzy evidence structures associated with multiple information sources. Finally, an effective decision rule is developed to take into account uncertainty, quantified by Shannon entropy and fuzzy entropy, of probabilistic evidence and fuzzy evidence, to deal with conflict and to achieve robust decisions. To demonstrate the effectiveness of this approach, we apply it to classify multimodality human brain MR images in a supervised manner. The proposed approach outperforms both the traditional evidential reasoning technique and the fuzzy reasoning technique, in terms of robustness and classification accuracy.

The paper presents a novel adaptive fuzzy evidential nearest neighbor formulation for classifying remotely sensed images. The formulation combines the generalized fuzzy version of the Dempster-Shafer evidence theory (DSET) and the K-nearest neighbor (KNN) algorithm. Each of the K nearest neighbors provides evidence on the belongingness of the input pattern to be classified, and it is evaluated based on a measure of disapproval to achieve the adaptive capability during the classification process. The disapproval measure quantifies the lack of support with respect to the belongingness of the input pattern to a given class. Pieces of evidence are ranked based on their degree of disapproval and fused in a sequential manner. The pignistic Shannon entropy is used to estimate the degree of consensus among pieces of evidence provided by nearest neighbors and as a criterion for terminating the evidence fusion process. The paper reports the results of experimental work conducted to evaluate the proposed classification scheme using real multichannel remote sensing images. As will be demonstrated using the experimental results, the proposed classification scheme demonstrated robust performance and outperformed commonly used methods such as the K-nearest neighbor algorithm of Cover and Hart (1967), the fuzzy K-nearest neighbor algorithm of Keller et al. (1985), the evidence-theoretic K-nearest neighbor algorithm of Denoex (1995), and its fuzzy version of Zouhal and Denoex (1997). The performance of these techniques is examined with respect to the K-parameter and classification accuracy.

Dempster-Shafer evidence theory (DSET) is a popular paradigm for dealing with uncertainty and imprecision. Its corresponding evidential reasoning framework is theoretically attractive. However, there are outstanding issues that hinder its use in real-life applications. Two prominent issues in this regard are 1) the issue of basic probability assignments (masses) and 2) the issue of dependence among information sources. This paper attempts to deal with these issues by utilizing neural networks in the context of pattern classification application. First, a multilayer perceptron neural network with the mean squared error as a cost function is implemented to calculate, for each information source, posteriori probabilities for all classes. Second, an evidence structure construction scheme is developed for transferring the estimated posteriori probabilities to a set of masses along with the corresponding focal elements, from a Bayesian decision point of view. Third, a network realization of the Dempster-Shafer evidential reasoning is designed and analyzed, and it is further extended to a DSET-based neural network, referred to as DSETNN, to manipulate the evidence structures. In order to tackle the issue of dependence between sources, DSETNN is tuned for optimal performance through a supervised learning process. To demonstrate the effectiveness of the proposed approach, we apply it to three benchmark pattern classification problems. Experiments reveal that the DSETNN out-performs DSET and provide encouraging results in terms of classification accuracy and the speed of learning convergence.

We live in a society that depends on high-tech devices for assistance with everyday tasks, including everything from transportation to health care, communication, and entertainment. Tedious tactile input interfaces to these devices result in inefficient use of our time. Appropriate use of natural hand gestures will result in more efficient communication if the underlying meaning is understood. Overcoming natural hand gesture understanding challenges is vital to meet the needs of these increasingly pervasive devices in our every day lives. This work presents a graph-based approach to understand the meaning of hand gestures by associating dynamic hand gestures with known concepts and relevant knowledge. Conceptual-level processing is emphasized to robustly handle noise and ambiguity introduced during generation, data acquisition, and low-level recognition. A simple recognition stage is used to help relax scalability limitations of conventional stochastic language models. Experimental results show that this graph-based approach to hand gesture understanding is able to successfully understand the meaning of ambiguous sets of phrases consisting of three to five hand gestures. The presented approximate graph-matching technique to understand human hand gestures supports practical and efficient communication of complex intent to the increasingly pervasive high-tech devices in our society.

A network realization of the Dempster-Shafer evidential reasoning is developed, and it is further extended to a neural network, referred to as DSETNN, for dealing with the dependence of evidential sources. DSETNN is tuned for optimal performance through a supervised learning process. To demonstrate the effectiveness of DSETNN, we apply it to two benchmark pattern classification problems. Experiments reveal that DSETNN outperforms the Dempster-Shafer evidential reasoning, the majority voting, single source based results, and provides encouraging results in terms of classification accuracy and the speed of learning convergence.

Image retrieval plays an important role in a broad spectrum of applications. Contentbased retrieval (CBR) is one of the popular choices in many biomedical and industrial applications. Discrete image transforms have been widely studied and suggested for many image retrieval applications. The Discrete Wavelet Transform (DWT) is one of the most popular transforms recently applied to many image processing applications. The Daubechies wavelet can be used to form the basis for extracting features in retrieving images based on the description of a particular object within the scene. This wavelet is widely used for image compression. In this paper we highlight the common features between compression and retrieval. Several examples are used to test the DWT retrieval system. A comparison between DWT and Discrete Cosine Transform (DCT) is also made. The retrieval system using DWT requires preprocessing and normalization of images, which might slow down the retrieval process. The accuracy of the retrieval using DWT has been significantly improved by incorporating efficient K-Neighbor Nearest Distance (KNND) measure in our system.

A supervised learning method is proposed to automatically extract fuzzy rules for numerical pattern classification problems. fuzzy rules are constructed corresponding to hyperboxes in a multi-dimensional feature space, where a hyperbox indicates an existence region of data belonging to a singleton class or a compound class. Hyperboxes are effectively realized by means of a linked list based region-merging technique. The method supports the representation of the union of multiple classes in the region merging process and hence it can deal with compound classes in the cases where highly mixed classes exist. Also, the method is capable of automatically deleting trivial features during the rule learning process. To demonstrate the effectiveness of the proposed method, experiments are carried out for classifying Iris data set and human brain magnetic resonance images (MRI). It is concluded that the proposed method performs well and is quite competitive to other fuzzy rule extraction techniques.

In this paper, we present an image segmentation approach, in which fuzzy reasoning is employed in two phases, namely, a denoising phase and a region merging phase. Denoising is realized adaptively, depending on local image characteristics via the use of a set of fuzzy rules. It is an iterative process that terminates based on the variation of information content in the denoised (filtered) image. The denoised image is processed using the Canny operator to generate a gradient magnitude image. This gradient magnitude image is then segmented using the watershed transform. A fuzzy reasoning driven similarity measure is then used to merge the regions resulted from the initial watershed transform segmentation. A genetic algorithm is employed to optimize the segmentation results. Experiments have shown that using the proposed approach, high quality denoising and segmentation performance can be achieved on natural as well as medical images.

We present a fuzzy evidential reasoning algorithm in light of the Dempster-Shafer evidence theory and the K-nearest neighbor algorithm for pattern classification. Given an input pattern to be classified, each of its K nearest neighbors is viewed as an evidence source, in terms of a fuzzy evidence structure. The distance between the input pattern and each of its K nearest neighbors is used for mass determination while the contextual information of the nearest neighbor in the training sample space is formulated by a fuzzy set in determining a fuzzy focal element. Therefore, pooling evidence provided by neighbors is realized by a fuzzy evidential reasoning, where feature selection is further considered through ranking and adaptive combination of neighbors. A fast implementation scheme of the fuzzy evidential reasoning is also developed. Experimental results of classifying multichannel remote sensing images have shown that the proposed approach outperforms the K-nearest neighbor (K-NN) algorithm [T.M. Cover et al. (1967)], the fuzzy K-nearest neighbor (F-KNN) algorithm [J.M. Keller et al. (1985)], the evidence-theoretic K-nearest neighbor (E-KNN) algorithm [T. Denoex (1995)], and the fuzzy extended version of E-KNN (FE-KNN) [L.M. Zouhal et al. (1997)], in terms of the classification accuracy and insensitivity to the number K of nearest neighbors.

There exist several industrial applications for large crane systems. Most of them experience serious problems with load swing. This paper presents a fuzzy based control scheme to minimize load swing for crane systems while maintaining continuous payload transportation. The control system of the crane is built using behavior-based approaches. In the control system developed, each module generates behaviors, and improvement in the performance of the system proceeds by adding new modules to the system. In order to develop the anti-swing module, fuzzy logic controller is applied using information extracted from potentiometers. The fuzzy controller provides a mechanism for dealing with imprecise sensor data. The anti-swing behaviors are successfully implemented by formulating a set of fuzzy rules. The performance of the developed system is illustrated by both simulations and experiments. The simulation and experimental results of the system show that the system remains stable under several operating situations.

We report on a fuzzy logic-based language understanding system applied to speech recognition. This system acquires conceptual knowledge from corpus data and organizes such knowledge into fuzzy logic inference rules. The system parses speech recognition results into conceptual structures in a robust manner, and thus is able to tolerate noise caused by speech recognition errors. We discuss the fuzzy inference rule learning method and explain its organization. Experimental results that demonstrate the ability of the system to deal with complex speech input instances are reported.

We examine in this article the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive hybrid intelligent control scheme is proposed here. The controller makes use of a multi-input multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed one does not require a precise model of the system's dynamics. The performance of the proposed controller is compared to that of a well known conventional adaptive controller.

The robustness of a given vision system in the field of robotics is a very challenging problem and represents a major bottleneck in any industrial setting. Nevertheless, there is a hypothesis that the fusion of multiple natural features facilitates a robust detection and object tracking in scenes of real world complexity. Several fusion methods have been tested for cue integration with good results, but the computational effort grows as the number of features increases. This research work represents a variant of the fusion method based both on distributed systems and on an agent concept. In this work, multiple agents interact with each other to perform different roles. The structure has a cooperative approach so that the agents work as a team. The communication among the agents is based on the Event Service of CORBA technology. By using this architecture, we are exploiting the parallelism and concurrency of distributed systems, and by using the concept of agents we are exploiting the encapsulation concept to built modular systems.

This paper presents a novel technique to fuse multi information sources for the purpose of pattern classification. The proposed data fusion technique is based on the Dempster-Shafer evidence theory. Mass functions are derived from probabilistic and fuzzy measures that are associated with discriminant functions for pattern classification. Simulated synthetic images as well as real human brain magnetic resonance images (MRI) are tested to demonstrate the performance and effectiveness of the proposed approach. It is concluded from the experimental results that the proposed algorithm is quite effective and superior to other approaches such as the Bayesian approach. Furthermore, the paper explains how this approach exhibits a capability to handle uncertainty, imprecision and conflicts which often hinders multi information fusion.

This paper presents a survey about different types of fuzzy information measures. A number of schemes have been proposed to combine the fuzzy set theory and its application to the entropy concept as a fuzzy information measurements. The entropy concept, as a relative degree of randomness, has been utilized to measure the fuzziness in a fuzzy set or system. However, a major difference exists between the classical Shannon entropy and the fuzzy entropy. In fact while the later deals with vagueness and ambiguous uncertainties, the former tackles probabilistic uncertainties (randomness).

A fuzzy integral based region merging algorithm is presented to deal with the issue of oversegmentation due to the watershed transform. The algorithms integrates region and edge features together using a fuzzy logic based fusion method. Firstly, preprocessing and watershed segmentation are performed on the image. Depending on the complexity of the image content the segmentation process may produce many more regions than what are really expected to exist in the image. To reduce the number of regions that results due to oversegmentation, a fusion process is applied to these regions recursively according to the principle of the maximum fuzzy integral. After transferring these features to memberships that reflect the degree that a given region to belong to its neighboring regions, an integration (fusion) scheme is used to compute a fuzzy integral based on which a decision is made with respect to the region merging. To evaluate the performance of the proposed approach it has been applied to real images such as MRI and natural images.

To be reliable a sensory system must be able to determine the likelihood of success of the different sensing strategies at its disposal. For this reliability estimation to be meaningful, it has to be dynamic so as to respond to any changes in the observation conditions. It is therefore desirable to develop assessment methods that can continuously evaluate the reliability of potential sensory strategies taking in consideration changes in the observation conditions. This paper proposes a novel approach for capturing the impact of observation conditions on the reliability of sensory systems. The approach revolves around a new information variation measure which provides the sensory system with a quantitative measure of the quality of the information gathered by its sensors. Since it can predict a more realistic estimate of the success rate based on the state of the observation conditions, the model provides valuable information that can be used to perform more effective sensor planning based on reliability considerations, and hence has the potential to enhance the capability of sensory systems in dealing with unstructured applications.

In this paper, a decentralized approach based on the team consensus approach and Markovian model is proposed to integrate multisensory data. A team of sensors can estimate the local and global uncertainties utilizing self-entropy and conditional-entropy measures of the sensors. Consensus can be reached based on the initial expected values and "uncertainty" weights assigned by the sensors. The proposed approach is compared with the Bayesian approach via experiments on two independent sensors. Results showed that consensus reached are comparable. However, there are factors that indicated the decentralized approach requires less communication and computational effort to reach consensus among sensors.

The authors investigate the issue of information interdependence in multisensor systems. A consensus group approach which accounts for interdependence between group sensors is presented. The approach is based on an information theory formulation. An algorithm for computing a weighting scheme for the group sensors is developed. The algorithm rewards each sensor for the quality of its measurements and penalizes it for its interdependence with other sensors in the group.

The authors presented an approach that can be used to integrate multisensory data. The approach treats the sensors of a multisensor system as a group of experts cooperating with each other to resolve environment ambiguities. The approach is based on a recursive team utility function that is capable of bringing the team members to a consensus. This approach is simple and computationally tractable. The approach can be applied to integrate observation in object identification, a prominent area in which multisensor systems have the potential to play an important role.>