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The SETUP09 system consists of both navigation and a computer-aided drawing technique for people who are blind and visually impaired (BVI). The purpose of this paper is to address the need for a screen navigation technique, which can facilitate a user’s ability to produce art, and scientific diagrams electronically, by introducing a compass-based screen navigation method.

BVI computer users were tested using different screen navigation tasks to assess the accuracy and efficiency of this compass-based navigation technique by using a prototype (SETUP09) and tactile paper grid maps.

The results confirmed that the compass-based navigation facilitates higher accuracy in screen-based moving and location recognition with a noticeable reduction in time and effort.

Improvements such as the addition of a sound layer to the interface, use of hotkeys, braille and user speech inputs are yet to be tested.

The current lack of suitable and efficient screen navigation technology is a limiting factor for BVI students and computer users in producing diagrams and drawings. This may place limitations on their career progression and life contentment. It is challenging for a BVI person to draw diagrams and art, which are commonly taught in education or used in industry. The compass-based screen navigation system was developed to address BVI users’ need to be able to create such content.

A compass-based navigation method enables screen navigation through a formal command language and enables intuitive movement to a screen location using matrix-style compass directions with zoom-in and zoom-out capabilities.

MOOsburg is a community‐oriented multi‐user domain. It was created to enrich the Blacksburg Electronic Village by providing real‐time, situated, interaction, and a place‐based information model for community information. We are experimenting with an implementation fundamentally different from classic multi‐user domains object‐oriented (MOOs), supporting distributed system development and management, and a direct manipulation approach to navigation. To guide the development of MOOsburg, we are focusing on a set of community‐oriented applications, including a virtual science fair.

This study aims to design a new low-cost localization platform for estimating the location and orientation of a pedestrian in a building. The micro-electro-mechanical systems (MEMS) sensor error compensation and the algorithm were improved to realize the localization and altitude accuracy.

The platform hardware was designed with common low-performance and inexpensive MEMS sensors, and with a barometric altimeter employed to augment altitude measurement. The inertial navigation system (INS) – extended Kalman filter (EKF) – zero-velocity updating (ZUPT) (INS-EKF-ZUPT [IEZ])-extended methods and pedestrian dead reckoning (PDR) (IEZ + PDR) algorithm were modified and improved with altitude determined by acceleration integration height and pressure altitude. The “AND” logic with acceleration and angular rate data were presented to update the stance phases.

The new platform was tested in real three-dimensional (3D) in-building scenarios, achieved with position errors below 0.5 m for 50-m-long route in corridor and below 0.1 m on stairs. The algorithm is robust enough for both the walking motion and the fast dynamic motion.

The paper presents a new self-developed, integrated platform. The IEZ-extended methods, the modified PDR (IEZ + PDR) algorithm and “AND” logic with acceleration and angular rate data can improve the high localization and altitude accuracy. It is a great support for the increasing 3D location demand in indoor cases for universal application with ordinary sensors.

The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.

A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.

The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.

MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

The authors describe a hypothetical course that educators can use as a resource and model to (1) inform students about the transformations currently occurring as societies grounded in practices of the 20th century Industrial Age experiment with the emergent systems and structures of the 21st century Innovation Age, (2) identify experiential learning strategies that actively engage students in practicing the collaboration skills they will need to be successful, and (3) expose students to the field of positive psychology to understand their psychological strengths and to learn how to use them strategically to enjoy more success across multiple social networks. These multiple social networks present a complexity to learners that require students to develop a navigational compass. Psychological strengths refer to personality traits and competencies that enable people to do things well. In this three module course, students learn how moments of positive emotion can contribute to the high levels of engagement that occur when operating from strengths. Awareness and use of strengths energize the drive for achievement, sustain resilience, and improve performance. Students systematically identify their strengths and learn to spot strengths in others. In portfolios, they document engaged experiences to understand what truly energizes them and improves productivity. They reflect on how strengths and moments of positive emotion affect their self-esteem and self-efficacy. In class activities, students explore how to deploy strengths effectively in different settings. In the last module, they set goals and work with teams to discover why collaboration and communication are essential to maximizing the value of strengths-based learning in social networks.

EDUCATION and training of maintenance technicians begins with selection of potentially suitable students. Selection where possible should be based on personal interview in respect of the advertised course entry qualifications — Certificate of Secondary Education or G.C.E. in Mathematics, English and a Science subject — and an assessment of the applicant in terms of interest, enthusiasm and integrity, career ambitions and prospects.

The Westland Lynx helicopter is a particularly fine example of the use of advanced fan technology in modern aircraft applications. The firm of Airscrew Howden have come a long way from their original manufacture of the wooden ‘prop’ but they still continue to play a very essential part in all types of aircraft flying today; this takes the form of sophisticated fan designs to cover a wide variety of special air‐movement requirements that can arise in this sector.

In spite of the vehicle, magnetic field interference can be reduced by some measures and techniques in ammunition design and manufacturing stage, the corruption of the vehicle magnetic field can still reach hundreds to thousands of nanoteslas. Besides, the magnetic field that the ferromagnetic materials generate in response to the strong magnetic field in the vicinity of the body. So, a real-time and accurate vehicle magnetic field calibration method is needed to improve the real-time measurement accuracy of the geomagnetic field for spinning projectiles.

Unlike the past two-step calibration method, the algorithm uses a linear model to calibrate the magnetic measurement error in real-time. In the method, the elliptical model of magnetometer measurement is established to convert the coefficients of hard and soft iron errors into the parameters of the elliptic equation. Then, the parameters are estimated by recursive least square estimator in real-time. Finally, the initial conditions for the estimator are established using prior knowledge method or static calibration method.

Studies show the proposed algorithm has remarkable estimation accuracy and robustness and it realizes calibration the magnetic measurement error in real-time. A turntable experiments indicate that the post-calibration residuals approximate the measurement noise of the magnetometer and the roll accuracy is better than 1°. The algorithm is restricted to biaxial magnetometers’ calibration in real-time as expressed in this paper. It, however, should be possible to broaden this method’s applicability to triaxial magnetometers' calibration in real-time.

Unlike the past two-step calibration method, the algorithm uses a linear model to calibrate the magnetic measurement error in real-time and the calculation is small. Besides, it does not take up storage space. The proposed algorithm has remarkable estimation accuracy and robustness and it realizes calibration the magnetic measurement error in real time. The algorithm is restricted to biaxial magnetometers’ calibration in real-time as expressed in this paper. It, however, should be possible to broaden this method’s applicability to triaxial magnetometers’ calibration in real-time.

This study aims to find a novel solution to the calibration of three-axis magnetometers to suppress errors of sensors. The nature of the calibration process is parameter estimation and hence the purpose of the paper is to calculate the error parameters and eliminate sensor errors and obtain the true value of the pure magnetic field.

The paper puts forward a calibration method using an alternative iteration looping optimization (AILO) to estimate the parameters. The proposed method divided the parameters to be estimated into two parts: a portion less than one and the other greater than one. Parameters with different orders of magnitude are calculated respectively, which let one part to be a known quantity and the other part is derived by the known quantity; the derived quantity is used to calculate the known quantity again, and looping iteration multiple times until the iteration termination condition is satisfied.

The simulation and experimental results indicate that the calibration accuracy is improved at least by two orders by the proposed method compared to the two-step method and the linear decreasing weight particle swarm optimization (LDW-PSO) algorithm which proves the validity of the proposed method.

The proposed method can improve the calibration accuracy of total magnetic field, which provides a reference to the calibration of three-axis magnetometers.

A calibration method based on the AILO is proposed in this paper, which is used to improve the calibration accuracy of the three-axis magnetometer.