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To solve the problem that the traditional received signal strength indicator real-time location method does not test the attenuation characteristics of the electromagnetic wave transmission in the location area, which cannot guarantee the accuracy of the location, resulting in a large location error.

At present, the compressed sensing (CS) reconstruction algorithm can be roughly divided into the following two categories (Zhouzhou and Fubao, 2014; Lagunas et al., 2016): one is the greedy iterative algorithm proposed for combinatorial optimization problems, which includes matching pursuit algorithm (MP), positive cross matching tracking algorithm (OMP), greedy matching tracking algorithm, segmented orthogonal matching tracking algorithm (StOMP) and so on. The second kind is the convex optimization algorithm, which also called the optimization approximation method. The common method is the basic tracking algorithm, which uses the norm instead of the norm to solve the optimization problem. In this paper, based on the piecewise orthogonal MP algorithm, the improved StOMP reconstruction algorithm is obtained.

In this paper, the MP algorithm (OMP), the StOMP and the improved StOMP algorithm are used as simulation reconstruction algorithms to achieve the comparison of location performance. It can be seen that the estimated position of the target is very close to the original position of the target. It is concluded that the CS grid-based target stepwise location method in underground tunnel can accurately locate the target in such specific region.

In this paper, the offline fingerprint database in offline phase of location method is established and the measurement of the electromagnetic noise distribution in different localization areas is considered. Furthermore, the offline phase shares the work of the location process, which greatly reduces the algorithm complexity of the online phase location process and the power consumption of the reference node, meanwhile is easy to implement under the same conditions, as well as conforms to the location environment.

Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve the performance of WiFi indoor positioning based on RSS, this paper aims to propose a novel position estimation strategy which is called radius-based domain clustering (RDC). This domain clustering technology aims to avoid the issue of access point (AP) selection.

The proposed positioning approach uses each individual AP of all available APs to estimate the position of target point. Then, according to circular error probable, the authors search the decision domain which has the 50 per cent of the intermediate position estimates and minimize the radius of a circle via a RDC algorithm. The final estimate of the position of target point is obtained by averaging intermediate position estimates in the decision domain.

Experiments are conducted, and comparison between the different position estimation strategies demonstrates that the new method has a better location estimation accuracy and reliability.

Weighted k nearest neighbor approach and Naive Bayes Classifier method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of the two strategies are affected by AP selection strategies and inappropriate selection of APs may degrade positioning performance considerably.

The RDC positioning approach can improve the performance of WiFi indoor positioning, and the issue of AP selection and related drawbacks is avoided.

The RSS-based effective WiFi indoor positioning system can makes up for the indoor positioning weaknesses of global navigation satellite system. Many indoor location-based services can be encouraged with the effective and low-cost positioning technology.

A novel position estimation strategy is introduced to avoid the AP selection problem in RSS-based WiFi indoor positioning technology, and the domain clustering technology is proposed to obtain a better accuracy and reliability.

Component positioning is an important part of aircraft assembly, aiming at the problem that it is difficult to accurately fall into the corresponding ball socket for the ball head connected with aircraft component. This study aims to propose a ball head adaptive positioning method based on impedance control.

First, a target impedance model for ball head positioning is constructed, and a reference positioning trajectory is generated online based on the contact force between the ball head and the ball socket. Second, the target impedance parameters were optimized based on the artificial fish swarm algorithm. Third, to improve the robustness of the impedance controller in unknown environments, a controller is designed based on model reference adaptive control (MRAC) theory and an adaptive impedance control model is built in the Simulink environment. Finally, a series of ball head positioning experiments are carried out.

During the positioning of the ball head, the contact force between the ball head and the ball socket is maintained at a low level. After the positioning, the horizontal contact force between the ball head and the socket is less than 2 N. When the position of the contact environment has the same change during ball head positioning, the contact force between the ball head and the ball socket under standard impedance control will increase to 44 N, while the contact force of the ball head and the ball socket under adaptive impedance control will only increase to 19 N.

In this paper, impedance control is used to decouple the force-position relationship of the ball head during positioning, which makes the entire process of ball head positioning complete under low stress conditions. At the same time, by constructing an adaptive impedance controller based on MRAC, the robustness of the positioning system under changes in the contact environment position is greatly improved.

Vision-based positioning without camera calibration, using uncalibrated industrial robots, is a challenging research problem. To address the issue, an uncalibrated industrial robot real-time positioning system has been developed in this paper. The paper aims to discuss these issues.

The software and hardware of this system as well as the methodology are described. Direct and inverse kinematics equations that map joint space into “camera space” are developed. The camera-space manipulation (CSM) algorithm has been employed and improved with varying weights on camera samples of the robot end effector, and the improved CSM is named VW-CSM. The experiments of robot positioning have been conducted using the traditional CSM algorithm and the varying-weight CSM (VW-CSM) algorithm, respectively, both without separate camera calibration. The impact on the accuracy and real-time performance of the system caused by different weights has been examined and discussed.

The experimental results show that the accuracy and real-time performance of the system with the VW-CSM algorithm is better than the one with using the original CSM algorithm, and the impact on the accuracy and real-time performance of the system caused by different weights has been revealed.

The accuracy and real-time performance of the system with the VW-CSM algorithm is verified. These results prove that the developed system using the VW-CSM algorithm can satisfy the requirements of most industrial applications and can be widely used in the field of industrial robots.

This article aims to examine positioning strategies of international and multicultural‐oriented service brands.

Following review of the literature and pilot study, three main populations (executives and experts, companies' marketing communications, and the target group of consumers) were examined. The methodology concerned triangulation research involving face‐to‐face long interviews, secondary data, content analysis and mail survey.

The paper highlights that while no single positioning strategy is significant across the four card brands (Visa, MasterCard, Amex and Diners Club), “the brand name” positioning strategy appears to be the most preferred among Visa, MasterCard and Amex and not Diners Club. The findings also show that “top of the range” positioning strategy is favored among Amex and Diners Club card brands. However, “country of origin” positioning strategy is incompatible within the study setting.

Apart from the low response rate from survey of the general public, another limitation of this study is the concentration on a single sector of the services industry. The latter poses difficulties for generalization across all service brands.

Service managers now have an insight into the positioning activities of the plastic card brand sector. These serve as building blocks and benchmarks for appreciating and operationalizing the concept of positioning – a research issue that is missing in the extant literature.

This study is a step forward in the operationalization of the concept of positioning. The research also provides diagnosis of the congruence between management's presumed positioning strategies, firm's actual positioning practices and target group's perceptions of the positioning strategies. Without such knowledge, managers cannot expect to choose the best competitive options to defend or enhance their positions in the market place.

This chapter provides an overview of the marketing strategy development process in the commercialization of breakthrough technologies. Important concepts and elements that are considered critical when developing market applications are presented with emphasis on three key decisions: target market selection, segmentation, and positioning. These strategic decisions will guide the more tactical considerations relating to the specific elements, or marketing mix, of the product’s marketing strategy. Marketing strategy development is a dynamic process that is impacted by many factors. This chapter highlights the dynamic nature of this process as well as providing insight as to the fundamental considerations in strategy formulation.

Wireless network localization technology is very popular in recent years and has attracted worldwide attention. The purpose of this paper is to improve the localization accuracy of ultra-wideband (UWB) with lower localization error taking into consideration the special real environment with the closed long and narrow space.

The principle of multidimensional scaling (MDS), particle swarm optimization (PSO) and Taylor series expansion algorithm (Taylor-D) were introduced. A novel positioning algorithm, MDS-PSO-Taylor was proposed to minimize the localization error. MDS-PSO algorithm provided a more accurate preliminary coordinate by applying the PSO algorithm so that the Taylor-D was used for further enhancing the localization accuracy.

Experimental results manifested that the proposed algorithm, providing small localization error value and higher positioning accuracy, can effectively reduce errors and achieve better performance in terms of the considerable improvement of localization accuracy.

The presented study with the real environment test attempts to demonstrate the proposed algorithm is hopeful to be applied to the underground environment for in the future.

The purpose of this study is to explore the extent to which love cues are used by brands targeted at multiple decision-makers in a family, specifically the mother and child.

First, secondary database (SmartyPants, 2013) is used to identify clusters of brands with similar benefit groups (i.e. health and nutrition food, indulgence food, entertainment and technology for entertainment and learning) that are most loved by mothers and/or children. Next, a content analysis of the ads for brands in these clusters is used to identify the common positioning cues across these clusters. The data from the content analysis are used to explore the extent to which love cues (along with functional and hedonic) are used by these brands loved by mothers and children.

The results of this study indicate that functional cues dominate the ads for the brands in functional product categories, as well as hedonic product categories. Love cues dominate the ads for functional brands preferred by only either moms or kids, whereas for hedonic brands, love cues dominate the ads targeted at both moms and kids.

The authors hope that this study provides an impetus for more empirical work toward understanding the role of love in positioning brands aimed at multiple family members.

Love, the underlying thread that connects a family, can be used by brand managers to appeal to multiple family members.

Families are fundamental to the society. The authors hope that this study helps marketers appreciate that and do a better job of marketing to the families, as families also form the fundamental units of purchase and consumption.

This study uses value congruency framework to look at the notion of love as a positioning theme for brands targeted at multiple decision-makers. Hence, the study contributes to the development of family decision-making behavior.

The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole.

Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN.

The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly.

The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology.

This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.

This study examines the activities and congruence of positioning strategies in the UK store card sector. Using a triangulation research methodology, the results confirm the prevalence of challenging and aggressive branding activities. The findings identify “Service”, “Value for money”, and “The Brand Name” as the most popular positioning strategies pursued by Marks & Spencer Card brand. In the case of Harrods Card brand, “Top of the range” and “The Brand Name” are the two dominant positioning strategies employed. The study further uncovers congruence between the two card brands' marketing “communications” efforts and consumers' “perceived” strategies. However, there is no congruence between managers' “presumed” strategies and the brands' marketing “communications” efforts. More specifically, although positioning activities in “communications” are successfully recognized by the target group, managers' positioning “presumptions/intentions” are not clearly transmitted in “communications”. Conclusions, managerial implications, limitations and future research directions are discussed.