Globally stable and high-performance Internet congestion control through a computational inspiration from nature

There are many reasons to worry that the current congestion control schemes in the Internet may be reaching its limits. Since the nature is a source of excellent solutions for complex problems, this work attempts to solve the congestion control problem, by adopting some biological principles and mechanisms. The current work proposes that the congestion problem in the Internet can be addressed through an inspiration from the population control tactics in nature. Toward this idea, each flow (W ) in the networ...     

Real-time embedded emotional controller

Recent studies show that emotion is a mechanism for fast decision-making in human and other animals. Mathematical models have been developed for describing emotion in mammals. These models, similar to other bioinspired models, must be implemented in embedded platforms for industrial and real applications. In this paper, brain emotional learning based intelligent controller, which is based on mammalian middle brain, is designed and implemented on field-programmable gate arrays, and this emotional controller is applied for controlling of laboratorial overhead traveling crane in model-free and embedded manner. The main features of this controller are leaning capability, providing a model-free control algorithm, robustness and the ability to respond swiftly. By designing appropriate stress signals, a designer can implement a proper trade among control objectives.     

Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA)

In this paper, fuzzy threshold values, instead of crisp threshold values, have been used for optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a single inverted pendulum having parameters with probabilistic uncertainties. The objective functions that have been considered are, namely, the normalized summation of rising time and overshoot of cart (S_RO–C) and the normalized summation of rising time and overshoot of pendulum (S_RO–P) in the deterministic approach. Accordingly, the probabilities of failure of those objective functions are also considered in the reliability-based design optimization (RBDO) approach. A new multi-objective uniform-diversity genetic algorithm (MUGA) is presented and used for Pareto optimum design of linear state feedback controllers for single inverted pendulum problem. In this way, Pareto front of optimum controllers is first obtained for the nominal deterministic single inverted pendulum using the conflicting objective functions in time domain. Such Pareto front is then obtained for single inverted pendulum having probabilistic uncertainties in its parameters using the statistical moments of those objective functions through a Monte Carlo simulation (MCS) approach. It is shown that multi-objective reliability-based Pareto optimization of the robust state feedback controllers using MUGA with fuzzy threshold values includes those that may be obtained by various crisp threshold values of probability of failures and, thus, remove the difficulty of selecting suitable crisp values. Besides, the multi-objective Pareto optimization of such robust feedback controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some optimum robust state feedback controllers can be compromisingly chosen from the Pareto frontiers.     

Toward the development of interactive virtual dissection with haptic feedback

Traditional, hands-on dissection of an animal is common practice in many classrooms to aid in the study of anatomy and biology. More specifically, virtual dissection environments have been developed making it possible to study the inner workings of animals without cutting them up. In this paper, we present a novel virtual reality dissection simulator, where a user can dissect an animal (i.e. frog) and its organs using a 3D force feedback haptic device. The simulator uses force feedback as part of a multimodal cue to provide guidance and performance feedback to the user. This paper highlights methodologies which are used for addressing some of the key challenges involved in designing and developing simulators, such as: modelling and mechanics of deformation, collision detection between multiple deformable bodies, and haptic feedback.     

Interactive multi-modal suturing

We present a mechanics-based interactive multi-modal environment designed as part of a serious gaming platform. The specific objectives are to teach basic suturing and knotting techniques for simple skin or soft tissue wound closure. The pre-wound suturing target, skin, or deformable tissue is modeled as a modified mass-spring system. The suturing material is designed as a mechanics-based deformable linear object. Tools involved in a typical suturing procedures are also simulated. Collision management modules between the soft tissue and the needle, the soft tissue and the suture are analyzed. In addition to modeling the interactive environment of a typical suturing procedure, basics of the modeling approaches on the evaluation of a stitch formed by the user are also discussed. For example, if needle insertion points are too close from each other or to the edge of the wound, when the suture is pulled, the suture will tear the soft tissue instead of suturing the incision together. Experiment results show that our simulator can run on a standard personal computer and allow users to perform different suturing patterns with smooth graphics and haptic feedback.     

An improved algorithm for layout design in cellular manufacturing systems

Layout has a significant role on the efficiency of manufacturing systems, but it has not received attention of researchers in comparison to cell formation in cellular manufacturing systems. In this paper, a mathematical model for facility layout in a cellular manufacturing system is proposed that minimizes both inter-cell and intra-cell material handling costs. A variant of simulated annealing algorithm is developed to solve the model. The developed algorithm produces solutions with better quality and less computation time in comparison with the benchmarked algorithm. The superiority of the algorithm in computation time is considerable especially when the size of the problem increases.     

An efficient optimal algorithm for the quantity discount problem in material requirement planning

An optimal algorithm based on branch-and-bound approach is presented in this paper to determine lot sizes for a single item in material requirement planning environments with deterministic time-phased demand and constant ordering cost with zero lead time, where all-units discounts are available from vendors and backlog is not permitted. On the basis of the proven properties of optimal order policy, a tree-search procedure is presented to construct the sequence of optimal orders. Some useful fathom rules have been proven, which make the algorithm very efficient. To compare the performance of this algorithm with the other existing optimal algorithms, an experimental design with various environments has been developed. Experimental results show that the performance of our optimal algorithm is much better than the performance of other existing optimal algorithms. Considering computational time as the performance measure, this algorithm is considered the best among the existing optimal algorithms for real problems with large dimensions (i.e. large number of periods and discount levels).     

Slippage control in soft finger grasping and manipulation

Handling objects with robotic soft fingers without considering the odds of slippage are not realistic. Grasping and manipulation algorithms have to be tested under such conditions for evaluating their robustness. In this paper, a dynamic analysis of rigid object manipulation with slippage control is studied using a two-link finger with soft hemispherical tip. Dependency on contact forces applied by a soft finger while grasping a rigid object is examined experimentally. A power-law model combined with a linear viscous damper is used to model the elastic behavior and damping effect of the soft tip, respectively. In order to obtain precise dynamic equations governing the system, two second-order differential equations with variable coefficients have been designed to describe the different possible states of the contact forces accordingly. A controller is designed based on the rigid fingertip model using the concept of feedback linearization for each phase of the system dynamics. Numerical simulations are used to evaluate the performance of the controller. The results reveal that the designed controller shows acceptable performance for both soft and rigid finger manipulation in reducing and canceling slippage. Furthermore, simulations indicate that the applied force in the soft finger manipulation is considerably less than the rigid “one.”.     

Blind spatial unmixing of multispectral images: New methods combining sparse component analysis,clustering and non-negativity constraints

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM+ and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method.     

Resistorless floating immittance function simulators employing current controlled conveyors and a grounded capacitor

In this paper, new floating immittance function simulators employing second-generation current controlled conveyors are proposed. The first four of the presented circuits employ only a single grounded capacitor as passive component and can realize either a negative or a positive floating inductor or capacitor. The last two of the proposed circuits do not employ passive components and can realize either negative or positive floating resistances. All of the proposed circuits do not require passive element matching. As an application, a third-order butterworth filter is realized using the proposed positive floating inductance simulator. SPICE simulation results and large signal behavior of the filter are included.