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The purpose of this study is to use visual and inertial sensors to achieve real-time location. How to provide an accurate location has become a popular research topic in the field of indoor navigation. Although the complementarity of vision and inertia has been widely applied in indoor navigation, many problems remain, such as inertial sensor deviation calibration, unsynchronized visual and inertial data acquisition and large amount of stored data.

First, this study demonstrates that the vanishing point (VP) evaluation function improves the precision of extraction, and the nearest ground corner point (NGCP) of the adjacent frame is estimated by pre-integrating the inertial sensor. The Sequential Similarity Detection Algorithm (SSDA) and Random Sample Consensus (RANSAC) algorithms are adopted to accurately match the adjacent NGCP in the estimated region of interest. Second, the model of visual pose is established by using the parameters of the camera itself, VP and NGCP. The model of inertial pose is established by pre-integrating. Third, location is calculated by fusing the model of vision and inertia.

In this paper, a novel method is proposed to fuse visual and inertial sensor to locate indoor environment. The authors describe the building of an embedded hardware platform to the best of their knowledge and compare the result with a mature method and POSAV310.

This paper proposes a VP evaluation function that is used to extract the most advantages in the intersection of a plurality of parallel lines. To improve the extraction speed of adjacent frame, the authors first proposed fusing the NGCP of the current frame and the calibrated pre-integration to estimate the NGCP of the next frame. The visual pose model was established using extinction VP and NGCP, calibration of inertial sensor. This theory offers the linear processing equation of gyroscope and accelerometer by the model of visual and inertial pose.

How to model blind image deblurring that arises when a camera undergoes ego-motion while observing a static and close scene. In particular, this paper aims to detail how the blurry image can be restored under a sequence of the linear model of the point spread function (PSF) that are derived from the 6-degree of freedom (DOF) camera’s accurate path during the long exposure time.

There are two existing techniques, namely, an estimation of the PSF and a blind image deconvolution. Based on online and short-period inertial measurement unit (IMU) self-calibration, this motion path has discretized a sequence of the uniform speed of 3-DOF rectilinear motion, which unites with a 3-DOF rotational motion to form a discrete 6-DOF camera’s path. These PSFs are evaluated through the discrete path, then combine with a blurry image to restoration through deconvolution.

This paper describes to build a hardware attachment, which is composed of a consumer camera, an inexpensive IMU and a 3-DOF motion mechanism to the best of the knowledge, together with experimental results demonstrating its overall effectiveness.

First, the paper proposes that a high-precision 6-DOF motion platform periodically adjusts the speed of a three-axis rotational motion and a three-axis rectilinear motion in a short time to compensate the bias of the gyroscope and the accelerometer. Second, this paper establishes a model of 6-DOF motion and emphasizes on rotational motion, translational motion and scene depth motion. Third, this paper addresses a novel model of the discrete path that the motion during long exposure time is discretized at a uniform speed, then to estimate a sequence of PSFs.

Lesson study (LS) is generally regarded as a pathway for teachers' professional development and a method for teachers' instructional research. LS has been regarded as having the potential to drive large-scale reform but little is known about how it does so from a district level. Therefore, this paper aims to reveal how lesson study promote district education reform.

This study offers an in-depth case study of how District Y of Shanghai, China, took LS as the primary method in promoting its District Project of Building Curriculum Leadership in Schools. By analyzing the key project documents and achievements in project promotion, and interviews with the major Project leaders at District and school levels, this study explored the practice and impact of LS as a tool to promote district reform.

In the District Project, LS has been a medium to address each individual school's real problems of practice and turn them into reform vision and reform will in alignment with District goals. Five levels of school curriculum texts have been planned, designed, translated, implemented, reflected on, updated and mutually adjusted systematically through LS to ensure consistency in transforming District reform vision into classroom practice. Different models of teaching-research community building were found in sampled project schools and professional expertise was built with district support to promote reform. The curriculum leadership development through LS has shaped reform leader schools and formed a collection of LS exemplars circulated in schools as high-quality curriculum packages, which laid the foundation for district-wide reform.

The innovative practice of LS in China's education reform has expanded its reach from within one classroom to the entire district curriculum system and made it an important tool to drive large-school district-based education reform.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

The purpose of this paper is to examine consumer behavior towards Greek convenience stores chains and to determine the criteria on which store selection is based. The paper also analyses the stores’ position in the retail sector.

A random sample of 360 convenience store consumers was surveyed across four major cities. Data were collected through personal interviews, using a structured questionnaire. Statistical analysis was applied to further evaluate the information.

Consumers choose convenience stores mainly to cover their daily needs and save time on shopping, despite their dissatisfaction with the provided services. The survey also identified the characteristics of convenience store customers and their preferences.

The principal contribution of the present research is its focus on consumer behavior towards convenience stores in Greece.