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A planar ultra-wideband (UWB) bandpass filter with widened upper stopband is presented in this paper. The filter is constructed by adopting a novel simplified composite right-/left-handed (SCRLH) transmission line (TL) structure. The fundamental characteristics of the SCRLH TL are first analyzed, and then its corresponding microstrip implementation is utilized to realize the practical UWB bandpass filter. However, thanks to the inherent parasitic effects of the microstrip quasi-lumped elements at high frequency, a spurious passband comes into being around 15.1 GHz. With an aim to broaden the bandwidth of the upper stopband, two embedded open-circuited stubs are introduced in the input and output feeding lines, which can effectively suppress the spurious passband, thus generating a widened upper stopband.

Two patients presenting with subcutaneous nodules, plaques, papules and ulceration caused by Trichophyton rubrum are described in this report. The first case was a 46-year-old woman referred with erythema and desquamation over her trunk, hands and feet for 30 years, progressing to nodules and ulceration over her trunk, arms and scalp for the last 2 years. The second case was a 34-year-old man who presented with a 2-year-history of itchy, erythema and desquamation over the trunk, progressing to papules, nodules and cyst around his ear, on the neck and scalp for 1 year. The diagnoses were suspected after direct microscopical examinations of the discharge materials, which revealed the presence of hyaline hyphae. The histological examinations showed granulomatous inflammatory infiltrates with fungal elements in the dermis including epithelioid cells, giant cells, lymphocytes and eosinophils, and the periodic acid-Schiff stain showed hypha within the granulomas. Cultures of puncture materials, skin biopsies and nails confirmed the diagnosis identifying T. rubrum. Antifungal therapies with itraconazole were successful in both patients, the lesions were completely clear with atrophic scars after 3 months. Side effects were not noticed during the medication. We discuss the clinical types of granulomatous cutaneous lesions caused by dermatophyte infections and evaluate the therapeutic effect of itraconazole.

In this paper, neural networks (NNs) and adaptive robust control design method are integrated to design a performance oriented control law with only output feedback for a class of single-input-single-output nth order nonlinear systems in a normal form. The nonlinearities in the system include repeatable unknown nonlinearities and nonrepeatable unknown nonlinearities such as external disturbances. In addition, unknown nonlinearities can exist in the control input channel as well. A high-gain observer is employed to estimate the states of system. All unknown but repeatable nonlinear functions are approximated by the outputs of multilayer neural networks with the estimated states as inputs to achieve a better model compensation. All NN weights are tuned on-line. In order to avoid possible divergence of on-line tuning, discontinuous projections with fictitious bounds are used in the weight adjusting law to make sure that all the weights are adapted within a prescribed range. Theoretically, the resulting controller achieves a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. Certain robust control terms is then constructed to effectively attenuate various model uncertainties and estimate errors. Furthermore, if all the states are available and the unknown nonlinear functions are in the functional ranges of the neural networks, an asymptotic output tracking is also achieved to retain the perfect learning capability of NNs in the ideal situation provided that the ideal NN weights fall within the prescribed range. The output feedback neural network adaptive robust control is then applied to the control of a linear motor drive system. Experiments are carried out to show the effectiveness of the proposed algorithm and the excellent output tracking performance.

In this paper, the recently proposed neural network adaptive robust control (NNARC) design axe generalized to synthesize performance oriented control laws for a class of nonlinear systems transformable to the semi-strict feedback forms through the incorporation of backstepping design techniques. All unknown but repeatable nonlinearities in system are approximated by outputs of multi-layer neural networks to achieve a better model compensation and an improved performance. Through the use of discontinuous projections with fictitious bounds, a controlled on-line training of all NN weights is achieved. Robust control terms can then be constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy.

In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance-oriented control laws for a class of single-input–single-output (SISO) nth-order non- linear systems. Both repeatable (or state dependent) unknown non-linearities and non-repeatable unknown non-linearities such as external disturbances are considered. In addition, unknown non-linearities can exist in the control input channel as well. All unknown but repeatable non-linear functions are approximated by outputs of multi-layer neural networks to achieve a better model compensation for an improved performance. All NN weights are tuned on-line with no prior training needed. In order to avoid the possible divergence of the on-line tuning of neural network, discontinuous projection method with fictitious bounds is used in the NN weight adjusting laws to make sure that all NN weights are tuned within a prescribed range. By doing so, even in the presence of approximation error and non-repeatable non-linearities such as disturbances, a controlled learning is achieved and the possible destabilizing effect of on-line tuning of NN weights is avoided. Certain robust control terms are constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. In addition, if the unknown repeatable model uncertainties are in the functional range of the neural networks and the ideal weights fall within the prescribed range, asymptotic output tracking is also achieved to retain the perfect learning capability of neural networks in the ideal situation. The proposed neural network adaptive control (NNARC) strategy is then applied to the precision motion control of a linear motor drive system to help to realize the high-performance potential of such a drive technology. NN is employed to compensate for the effects of the lumped unknown non-linearities due to the position dependent friction and electro-magnetic ripple forces. Comparative experiments verify the high-performance nature of the proposed NNARC. With an encoder resolution of 1 µm, for a low-speed back-and-forth movement, the position tracking error is kept within ±2 µm during the most execution time while the maximum tracking error during the entire run is kept within ±5.6 µm. Copyright © 2001 John Wiley & Sons, Ltd.

The systematical report presents our studies on the gecko that integrated biological fundaments for biomimetics – morphology, locomotive mechanics, neural signal decoding and adhesive mechanism that developed a gecko-inspired robot and gecko-robot. A 3 dimensional locomotion gait for a gecko moving on the floor, wall and ceiling was obtained by using one high speed camera recording and image processing. The effect of electric potential on the adhesion was studied. The gecko’s foot-toe, a 22 degree-of-freedom motion system, was modulated by stimulating the three neurons on the limb. A stereotaxis method was proposed and the instrument was developed. The brain atlas for the gecko was primarily set up. Based on biological understanding, a bio-inspired geckolike robot was developed and the characteristics of the mechanics were investigated. The robot’s gait and motion coordination was introduced from the three dimensional gait results. Locomotion of the gecko was modulated by implanting electrodes into the gecko’s middle brain and stimulating by a wireless controlling system.

Staphylococcus aureus has a peculiar ability to colonize the skin of patients with eczema and atopic dermatitis (AD), and is consistently found in eczematous skin lesions in these patients. A correlation between the severity of the eczema and colonization with S. aureus has been demonstrated, and it has been determined that bacterial colonization is an important factor aggravating skin lesions. Patients colonized with S. aureus have been treated with antibiotics in several open and double-blind placebo-controlled studies, with conflicting results.To investigate the colonizing features of S. aureus in the lesional and nonlesional skin of patients with eczema and AD in China and to compare the therapeutic effect of mupirocin plus hydrocortisone butyrate with vehicle ointment plus hydrocortisone butyrate.A multicentre, double-blind randomized trial was conducted. Eczema Area and Severity Index (EASI) scores were evaluated before the start of the trial and on the 7th, 14th and 28th day of treatment. Swabs for bacterial isolation were taken from lesional skin before the start of the trial and on the 7th, 14th and 28th day of treatment, and from nonlesional skin only before the start of the trial. A combination topical therapy with mupirocin plus hydrocortisone butyrate ointment was used in the experimental group, with vehicle ointment plus hydrocortisone butyrate ointment as a control.Of 327 patients enrolled in the study, 208 had eczema and 119 had AD. Bacteria were isolated from 70.2% of lesional and 32.7% of nonlesional skin samples from patients with eczema, of which S. aureus accounted for 47.3% and 27.9%, respectively. Bacteria were isolated from 74.8% of lesional and 34.5% of nonlesional skin samples from patients with AD, of which S. aureus accounted for 79.8% and 80.5%, respectively. The colonization density of S. aureus was markedly higher in lesional than in nonlesional skin, both in patients with eczema and with AD (P0.01, P0.05), and was positively correlated with lesion severity. Considering the EASI scores before and after treatment and the final effective rate, good therapeutic effects were obtained in both the combination experimental groups and the control groups (P0.01), and there were no differences in the global therapeutic effect between the two groups in patients with eczema and with AD (P0.05). However, in patients with eczema with a clinical score of8 or in patients with AD with a clinical score of7, the therapeutic effect in the experimental groups was superior to that in the control groups (P0.05) on the 7th day of treatment. There were no differences between the two groups on the 14th and 28th days of treatment (P0.05). Following the improvement of symptoms and signs of eczema and AD, the positive rates of bacteria and S. aureus were reduced on the 7th day of treatment.This study confirmed that lesional skin of patients with eczema and AD was more frequently colonized with S. aureus than was nonlesional skin. The more severe the eczema, the higher the colonization rate of S. aureus, and S. aureus was also more often present in lesional and nonlesional skin in patients with AD than in those with eczema. Staphylococcus aureus infection is related to the pathogenesis of eczema and AD. An antibiotic-corticosteroid combination and corticosteroid alone both gave good therapeutic effect in eczema and in AD, and both reduced colonization by S. aureus. Early combined topical therapy is beneficial to patients with moderate to severe eczema and AD, and it is unnecessary to use antibiotics at later stages of disease or in mild eczema or AD.

In this paper, an indirect neural network adaptive robust control (INNARC) scheme is developed for the precision motion control of linear motor drive systems. The proposed INNARC achieves not only good output tracking performance but also excellent identifications of unknown nonlinear forces in system for secondary purposes such as prognostics and machine health monitoring. Such dual objectives are accomplished through the complete separation of unknown nonlinearity estimation via neural networks and the design of baseline adaptive robust control (ARC) law for output tracking performance. Specifically, recurrent neural network (NN) structure with NN weights tuned on-line is employed to approximate various unknown nonlinear forces of the system having unknown forms to adapt to various operating conditions. The design is actual system dynamics based, which makes the resulting on-line weight tuning law much more robust and accurate than those in the tracking error dynamics based direct NNARC designs in implementation. With a controlled learning process achieved through projection type weights adaptation laws, certain robust control terms are constructed to attenuate the effect of possibly large transient modelling error for a theoretically guaranteed robust output tracking performance in general. Experimental results are obtained to verify the effectiveness of the proposed INNARC strategy. For example, for a typical point-to-point movement, with a measurement resolution level of ±1μm, the output tracking error during the entire execution period is within ±5μm and mainly stays within ±2μm showing excellent output tracking performance. At the same time, the outputs of NNs approximate the unknown forces very well allowing the estimates to be used for secondary purposes such as prognostics.Copyright © 2002 by ASME

A fuzzy adaptive robust tracking controller for a class of uncertain nonlinear dynamical systems is proposed and analyzed. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. A robust feedback component is employed to eliminate the effects of disturbances, while a fuzzy logic component equipped with an adaptation mechanism reduces modeling uncertainties by approximating the model's nonlinearities on-line. A projection method is used to prevent the adaptation parameters from going unbounded in the presence of disturbances. It is shown that the closed-loop system driven by the proposed controller is stable and the adaptation parameters are bounded. A guaranteed transient performance and a guaranteed final tracking accuracy in the presence of parametric uncertainties and disturbances are achieved. Furthermore, if there are no disturbances and the unknown model's nonlinearities are within the approximation range of the fuzzy logic system, asymptotic output tracking is also achieved.