http://ieeexplore.ieee.org TOC Alert for Publication# 5326 2009 November 06 40 1 C1 C1 35 40 1 C2 C2 37 The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements. ]]> 40 1 1 12 1299 As viewpoint issue is becoming a bottleneck for human motion analysis and its application, in recent years, researchers have been devoted to view-invariant human motion analysis and have achieved inspiring progress. The challenge here is to find a methodology that can recognize human motion patterns to reach increasingly sophisticated levels of human behavior description. This paper provides a comprehensive survey of this significant research with the emphasis on view-invariant representation, and recognition of poses and actions. In order to help readers understand the integrated process of visual analysis of human motion, this paper presents recent development in three major issues involved in a general human motion analysis system, namely, human detection, view-invariant pose representation and estimation, and behavior understanding. Public available standard datasets are recommended. The concluding discussion assesses the progress so far, and outlines some research challenges and future directions, and solution to what is essential to achieve the goals of human motion analysis. ]]> 40 1 13 24 653 The last decades a variety of portable or wearable navigation systems have been developed to assist visually impaired people during navigation in known or unknown, indoor or outdoor environments. There are three main categories of these systems: Electronic travel aids (ETAs), electronic orientation aids (EOAs), and position locator devices (PLDs). This paper presents a comparative survey among portable/wearable obstacle detection/avoidance systems (a subcategory of ETAs) in an effort to inform the research community and users about the capabilities of these systems and about the progress in assistive technology for visually impaired people. The survey is based on various features and performance parameters of the systems that classify them in categories, giving qualitative–quantitative measures. Finally, it offers a ranking, which will serve only as a reference point and not as a critique on these systems. ]]> 40 1 25 35 935 Electronic textiles (e-textiles), known as computational fabrics, offer an emerging platform for constructing ambient intelligent applications. Computational nodes in e-textiles are driven by batteries. Unlike wireless sensor networks, not each computational node in e-textiles has its own battery. Instead, many computational nodes in e-textiles share a battery. Existing e-textiles use one fixed battery to drive a fixed set of computation nodes (or power consuming electronic components). The fixed battery-component connection may result in electronic components stopping functioning and/or energy waste in batteries when link connection problems occur. In this paper, we propose a new infrastructure of the power networks for e-textiles: flexible power network (FPN). Under the FPN infrastructure, a power consuming node (PCN) is not just connected to one single fixed battery. Instead, it is connected to multiple batteries and can obtain power energy from one of the available battery nodes (BNs) with the help of a battery selector. The electrical features of battery selectors and overcurrent protectors that protect the batteries from wasting the charge when short-circuit faults occur are illustrated. Moreover, by modeling the number of fault occurrence at conductive wires and nodes stochastically, an evaluation algorithm is proposed to analyze the reliability of FPN and to compare the metrics of different design schemes under the perspective of both the BNs and the PCNs. Experimental results show that our FPN is more dependable than some common e-textile electric networks published before with the occurrence of short- and/or open-circuit faults. ]]> 40 1 36 51 1168 In recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change. ]]> 40 1 52 63 449 The multimode resource-constrained project-scheduling problem with discounted cash flows (MRCPSPDCF) is important and challenging for project management. As the problem is strongly nondeterministic polynomial-time hard, only a few algorithms exist and the performance is still not satisfying. To design an effective algorithm for the MRCPSPDCF, this paper proposes an ant colony optimization (ACO) approach. ACO is promising for the MRCPSPDCF due to the following three reasons. First, MRCPSPDCF can be formulated as a graph-based search problem, which ACO has been found to be good at solving. Second, the mechanism of ACO enables the use of domain-based heuristics to accelerate the search. Furthermore, ACO has found good results for the classical single-mode scheduling problems. But the utility of ACO for the much more difficult MRCPSPDCF is still unexplored. In this paper, we first convert the precedence network of the MRCPSPDCF into a mode-on-node (MoN) graph, which becomes the construction graph for ACO. Eight domain-based heuristics are designed to consider the factors of time, cost, resources, and precedence relations. Among these heuristics, the hybrid heuristic that combines different factors together performs well. The proposed algorithm is compared with two different genetic algorithms (GAs), a simulated annealing (SA) algorithm, and a tabu search (TS) algorithm on 55 random instances with at least 13 and up to 98 activities. Experimental results show that the proposed ACO algorithm outperforms the GA, SA, and TS approaches on most cases. ]]> 40 1 64 77 774 Multiagent systems consist of a collection of agents that directly interact usually via a form of message passing. Information about these interactions can be analyzed in an online or offline way to identify clusters of agents that are related. The first part of this paper is dedicated to a formal definition of a proposed dynamic model for agent clustering and experimental results that demonstrate applicability of this novel approach. The main contribution is the ability to discover and visualize communication neighborhoods of agents at runtime, which is a novel approach not attempted so far. The second part of this paper deals with a static agent clustering problem where equally sized clusters with maximal intracluster communication among agents are sought in order to efficiently distribute agents across multiple execution units. The weakness of standard clustering approaches for solving this type of clustering problem is shown. First, these algorithms optimize the generated clustering with respect to just one criterion, and therefore, yield solutions with inferior quality relative to the other criteria. Second, the algorithms are deterministic; thus they can produce just a single solution for the given data. A multiobjective clustering approach based on an iterative optimization evolutionary algorithm called multiobjective prototype optimization with evolved improvement steps (mPOEMS) is proposed and its advantages are demonstrated. The most important observation is that mPOEMS produces numerous high-quality solutions in a single run from which a user can choose the best one. The best solutions found by mPOEMS are significantly better than the solutions generated by the compared clustering algorithms. ]]> 40 1 78 86 544 This paper presents an algorithm for in-surveillance and out-of-surveillance mobile ground target tracking. In this respect, a nonlinear Bayesian estimation filter is presented. Then, an adaptive algorithm is derived to reduce remarkably the computational burden of this filter, while not degrading the accuracy of the state estimates. Additionally, an algorithm employing the concepts of hospitability and synthetic inclination maps is introduced and is coupled with the nonlinear Bayesian filter to track the mobile ground target once it goes out-of-surveillance. Loosely speaking, the hospitability map can be viewed as a terrain-based map defining a likelihood or a “weight” for each point on the earth's surface proportional to the ability of the target to move and maneuver at that location. On the other hand, the synthetic inclination map describes how the target favors certain regions within the search area, hence being “synthetically” inclined to move toward them. ]]> 40 1 87 97 677 A strategy is introduced to rank and select principal component transform (PCT) and discrete cosine transform (DCT) transform coefficient features to overcome the curse of dimensionality frequently encountered in implementing multivariate signal classifiers due to small sample sizes. The criteria considered for ranking include the magnitude, variance, interclass separation, and classification accuracies of the individual features. The feature ranking and selection strategy is applied to overcome the dimensionality problem, which often plagues the implementation and evaluation of practical Gaussian signal classifiers. The applications of the resulting PCT- and DCT-Gaussian signal classification strategies are demonstrated by classifying single-channel tongue-movement ear-pressure signals and multichannel event-related potentials. Through these experiments, it is shown that the dimension of the feature space can be decreased quite significantly by means of the feature ranking and selection strategy. The ranking strategy not only facilitates overcoming the dimensionality curse for multivariate classifier implementation but also provides a means to further select, out of a rank-ordered set, a smaller set of features that give the best classification accuracies. Results show that the PCT- and DCT-Gaussian classifiers yield higher classification accuracies than those reported in previous classification studies on the same signal sets. Among the combinations of the two transforms and four feature selection criteria, the PCT-Gaussian classifiers using the maximum magnitude and maximum variance selection criteria gave the best classification accuracies across the two sets of classification experiments. Most noteworthy is the fact that the multivariate Gaussian signal classifiers developed in this paper can be implemented without having to collect a prohibitively large number of training signals simply to satisfy the dimensio- nality conditions. Consequently, the classification strategies can be beneficial for designing personalized human–machine interface signal classifiers for individuals from whom only a limited number of training signals can reliably be collected due to severe disabilities. ]]> 40 1 98 108 734 In this paper, a model of a humanoid biped is developed. The dynamics are formulated to simulate responses to a sudden backwards translational disturbance of the support surface. The effect of joint stiffnesses, the role of vestibular and proprioceptive sensory apparatus in the maintenance of balance, and the involvement of the centers of mass and pressure are taken into consideration and shown in a number of simulations. Toward this end, a three-link sagittal biped with three muscle pairs at the ankle, knee, and hip, and two pairs of two-jointed muscles corresponding to quadriceps–hamstring and the gastrocnemius–antagonist group is subjected to computational experiments. Excursions of the center of gravity and the center of pressure are compared under different conditions. Comparisons to biological results are also discussed. ]]> 40 1 109 119 243 40 1 120 120 122 40 1 C3 C3 29 40 1 C4 C4 36