Improved phase performance for rotman lens

Rotman lenses are used to obtain multiple beams from a single array. Although the beams produced by the feed antennas at focal points have no path length errors, the beams produced by feed antennas at off focal points may have large path length errors. These path length errors cause deterioration in the multiple beams. In this article, two novel methods are introduced to obtain feed curves which reduce the path length errors of off focal feed points significantly, compared with the commonly used circular and elliptical feed curves. The first method obtains feed curve points based on having zero path length error at three chosen points of the radiating array for each beam direction. The second method uses the particle swarm optimization method for obtaining optimum feed points for each beam direction. The results show that there is a very significant drop in the level of the maximum path length errors (in the order of about 1:4). © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 23: 634–638, 2013.     

An LMI approach to synthesis subject to almost asymptotic regulation constraints

A multi-objective controller synthesis problem is considered in which an output is to be regulated approximately by assuring a bound on the steady-state peak amplification in response to an infinite-energy disturbance, while also guaranteeing a desired level of performance measured in terms of the worst-case energy gain from a finite-energy input to a performance output. Relying on a characterization of the controllers with which almost asymptotic regulation is accomplished, the problem of guaranteeing the desired level of performance is reduced to solving a system of linear matrix inequalities subject to a set of linear equality constraints. Based on the solution of this system, a procedure is outlined for the construction of a suitable controller whose order is equal to the order of the plant plus the order of the exogenous system.     

Coping with production time variability via dynamic lead-time quotation

In this paper, we propose two dynamic lead-time quotation policies in an M/GI/1 type make-to-stock queueing system serving lead-time sensitive customers with a single type of product. Incorporating non-exponential service times in an exact method for make-to-stock queues is usually deemed difficult. Our analysis of the proposed policies is exact and requires the numerical inversion of the Laplace transform of the sojourn time of an order to be placed. The first policy assures that the long-run probability of delivering the product within the quoted lead-time is the same for all backlogged customers. The second policy is a refinement of the first which improves the profitability if customers are oversensitive to even short delays in delivery. Numerical results show that both policies perform close to the optimal policy that was characterized only for exponential service times. The new insight gained is that the worsening impact of the production time variability, which is felt significantly in systems accepting all customers by quoting zero lead times, decreases when dynamic lead-time quotation policies are employed.     

Rule extraction from artificial neural networks to discover causes of quality defects in fabric production

In this paper, a novel classification rule extraction algorithm which has been recently proposed by authors is employed to determine the causes of quality defects in a fabric production facility in terms of predetermined parameters like machine type, warp type etc. The proposed rule extraction algorithm works on the trained artificial neural networks in order to discover the hidden information which is available in the form of connection weights in them. The proposed algorithm is mainly based on a swarm intelligence metaheuristic which is known as Touring Ant Colony Optimization (TACO). The algorithm has a hierarchical structure with two levels. In the first level, a multilayer perceptron type neural network is trained and its weights are extracted. After obtaining the weights, in the second level, the TACO-based algorithm is applied to extract classification rules. The main purpose of the present work is to determine and analyze the most effective parameters on the quality defects in fabric production. The parameters and their levels which give the best quality results are tried to be discovered and evaluated by making use of the proposed algorithm. It is also aimed to compare the accuracy of proposed algorithm with several other rule-based algorithms in order to present its competitiveness.     

Use of center of gravity with the common vector approach in isolated word recognition

In this paper, the subspace based classifier, common vector approach (CVA), with the center of gravity (COG) method is used for isolated word recognition. Since the CVA classifier is sensitive to shifts through the time axis, endpoint detection becomes extremely important for the recognition of isolated words. The COG method eliminates the need for endpoint detection. The effects of the COG method and a classical endpoint detection algorithm on the recognition rates of isolated words are investigated. The experimental results show that the COG method yields slightly higher recognition rates than the endpoint detection method in the TI-digit database when CVA is used.     

Mobility-Aware RSVP: A framework for improving the performance of multimedia services over wireless IP-based mobile networks

This paper proposes the Mobility-Aware Resource Reservation Protocol (MARSVP) in which mobility and QoS signaling are performed as a single functional block. The key concept of MARSVP is to convey mobility-specific information (binding updates and their associated acknowledgments) by using newly defined RSVP objects embedded in existing RSVP messages. An appealing feature of MARSVP is that it adheres to the current RSVP standard (RFC 2205) and thus requires minimal changes to end nodes without affecting any of the conventional RSVP routers in between. The proposed mechanism is evaluated using a simulation model for application-level performance and an analytical model for network-level signaling cost. Simulation results indicate a 27.9% improvement in QoS interruption when using Mobile IPv6 (MIPv6), 12.5% when using Hierarchical Mobile IPv6 (HMIPv6), and no improvement when using Fast Handovers for MIPv6 (FMIPv6). On the network-level, signaling cost savings of 9.4% and 11.9% are achieved for MIPv6 and HMIPv6, respectively, while FMIPv6 achieves savings of 17.9% when using Voice-over-IP traffic and 26.7% for Video-over-IP traffic. The results of the conducted studies indicate MARSVP’s superiority to conventional RSVP when deployed over wireless networks.     

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.     

基于COM与N层B/S结构的SNMP网管研究与实现*

分析了传统单一C/S结构的不足以及新型N层B/S结构的优越性,阐述了基于Web技术的网络管理是未来网络管理的发展趋势,结合现代软件开发中的软件复用理论,提出并设计实现了一种基于COM技术与N层B/S结构的SNMP网管系统模型,着重叙述了该系统中所使用的关键技术与设计要点。     

快速移植的微型呼叫中心设计

介绍了如何构建微型呼叫中心的系统设计及组成,给出了相应的系统结构,并对微型呼叫中心核心部分的设计进行了描述。该设计方案的优点是投资少,软件开发和扩展均较容易,实施快速,特别适合接入线路不多的微型呼叫中心。     

Multirobot coordination in pick-and-place tasks on a moving conveyor

This paper considers the problem of multirobot coordination in pick-and-place tasks on a conveyor band. The robot team is composed of identical robots with mutually exclusive, but neighboring workspaces. The products are fed in at one end of the band, move through each workspace sequentially until being picked up and are collected at the other end—if not picked up interim. Each robot has the same task—that is picking up and packaging as many products as possible. We propose an approach based on noncooperative game theory where each robot uses local observations of the conveyor band and their neighbors' actions in order to decide on its actions. The developed algorithm has been implemented and tested in a simulated manufacturing environment using Webots. Results obtained from the simulations are analyzed using a variety of statistical performance measures.