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The use of the Internet for Web‐based teaching and learning is fast becoming a reality. However, since it is difficult to verify the identity of the student through a simple user ID and password system on the client side, performance evaluation through test and examination through the Internet is still in its infancy. To overcome this main hurdle, a system has been designed and developed where a camera at the client computer is used to capture the student’s face and posture at random intervals during the test. The captured images are stored at the server and can be used to verify the identity of the person taking the test if the need arises. The system developed has been successfully used in a randomized multiple choice test in a course on analog and digital signals involving 450 students.

The purpose of this paper is to improve an image defogging algorithm based on a dark channel prior and use this method to clear the foggy image on the Advanced RISC (Reduced Instruction Set Computing) Machines (ARM) platform.

The divided strategy of the foggy image was proposed through the estimation of the brightness and transmission thresholds. The two regions of the foggy image were processed using two transmissions. Many foggy images were tested using the improved method.

The finding resulting from this study is that a divided strategy has been proposed to use the image defogging. Compared with the existing methods, the running time of the improved method is less on the ARM platform.

Image enhancement is an important technology of digital images, and the quality of images plays a key role in the video monitoring and the intelligent transportation system.

This paper presented an improved image defogging method using the divided strategy and a substantial number of experimental results was provided to demonstrate this method.

To face the challenges raised by the high incidence of falls among older adults, the intelligent video-monitoring system (IVS), a fall detection system that respects privacy, was developed. Most fall detection systems are tested only in laboratories. The purpose of this paper is to test the IVS in a simulation context (apartment-laboratory), then at home.

This study is a proof of concept including two phases: a simulation study to test the IVS in an apartment-laboratory (29 scenarios of activities including falls); and a 28-day pre-test at home with two young occupants. The IVS’s sensitivity (Se), specificity (Sp), accuracy (A) and error rate (E) in the apartment-laboratory were calculated, and functioning at home was documented in a logbook.

For phase 1, results are: Se =91.67 per cent, Sp =99.02 per cent, A=98.25 per cent, E=1.75. For phase 2, the IVS triggered four false alarms and some technical dysfunctions appeared (e.g. computer screen never turning off) that are easily overcome.

Results show the IVS’s efficacy at automatically detecting falls at home. Potential issues related to future installation in older adults’ homes were identified. This proof of concept led to recommendations about the installation and calibration of a camera-based fall detection system.

This paper highlights the potentialities of a camera-based fall detection system in real-world contexts and supports the use of the IVS to help older adults age in place.

The high-efficient processing of mass data is a primary issue in building and maintaining security video surveillance system. This paper aims to focus on the architecture of security video surveillance system, which was based on Hadoop parallel processing technology in big data environment.

A hardware framework of security video surveillance network cascaded system (SVSNCS) was constructed on the basis of Internet of Things, network cascade technology and Hadoop platform. Then, the architecture model of SVSNCS was proposed using the Hadoop and big data processing platform.

Finally, we suggested the procedure of video processing according to the cascade network characteristics.

Our paper, which focused on the architecture of security video surveillance system in big data environment on the basis of Hadoop parallel processing technology, provided high-quality video surveillance services for security area.

Three‐dimensional printing (3DP) is an increasingly popular additive manufacturing (AM) process. The structure produced in 3DP comprises of two main elements: an external “shell” and an inner “core”. The variation of this structure and print strategy dictates many factors, such as the final physical properties, the final weight of the part, the total material usage, effects on warpage and the build speed. As such, the accuracy and repeatability of these geometric structures is of importance. The measurement and validation of the actual printed structure is especially challenging due to the nature of the materials system. The purpose of this paper is to present an effective method to analyze the internal structure of a 3DP.

A dedicated video‐monitoring system has been developed to capture and characterize the 3DP build structure layer‐by‐layer. A significant image‐processing phase involved image calibration, filtering, thresholding and segmentation. The investigation is composed by three substudies. First, the reliability of the developed system was determined by comparing nominal dimensions of a benchmark part with video and contact measurements. The two studies have focused on the “shell” and “core” characterization, respectively.

A resolution of 508 pixel per inch was determined. From the first studies, benchmark elements of 0.5 mm presented a deviation between 0.29 and 0.44 mm from their normal dimension. The thickness of the external shell was analyzed, in both clear and coloured modes. Dimensions ranged between 1.51 and 1.58 mm for a clear part, and 1.59 and 1.69 mm for the coloured version. A further study resulted in a 3D virtual model of the internal mesh structure, which had been printed at high saturation.

The internal structure of a 3D printed part has been successfully analyzed by in‐process monitoring providing information and data not available through conventional analysis of the final part. This system provides a solution for real‐time non‐destructive analysis, which is currently absent in various forms of AM.

Older adults are at a high risk of falling. The consequences of falls are worse when the person is unable to get up afterward. Thus, an intelligent video monitoring system (IVS) was developed to detect falls and send alerts to a respondent. This study aims to explore the implementation of the IVS at home.

A multiple case study was conducted with four dyads: older adults and informal caregivers. The IVS was implemented for two months at home. Perceptions of the IVS and technical variables were documented. Interviews were thematically analyzed, and technical data were descriptively analyzed.

The rate of false alarms was 0.35 per day. Participants had positive opinions of the IVS and mentioned its ease of use. They also made suggestions for improvement.

This study showed the feasibility of a two-month implementation of this IVS. Its development should be continued and tested with a larger experimental group.

The purpose of this paper is to timely control of a construction collapse accident effectively during its development process by constructing a stage model and then aligning IT with each stage to help provide the information for decision making.

Through comprehensive literature review, this paper first identifies the various IT applications in on-site construction monitoring and analyzes the existed disaster/crisis stage models, also the stage models are compared with the causation models to illustrate the strengths. Then, a three-step methodology was conducted to develop and apply the conceptual framework, including the construction of the four-stage model; the establishment of the conceptual framework of information technology (IT) support for management of construction accidents (ITSMCA); and a building collapse accident used to illustrate the proposed framework.

The accident is divided into four stages, which are incubation stage, outbreak stage, spreading stage and final stage. The real-time staged information to support decision making, such as the contributing factors of on-site workers, materials, equipment and workplace, can be provided by emerging IT. Therefore, IT is aligned with the variations of contributing factors’ attributes in the four stages and ITSMCA is constructed to help accidents management.

The focus of the framework presented in this paper is that the stage model is effective for it catches the variations of the attributes whose values can be provided by IT rather than research on the practical application of the IT system. The construction and application of the IT system will be the research focus in the future.

This paper presents a stage model of a building collapse accident and gives a comprehensive conceptual framework of ITSMCA, which align the IT with different stages of the collapse accident. The ITSMCA proposes a feasible ideology and practical method for real-time management of the collapse accident during the process.

In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The purpose of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG and Harris are used for the process of feature extraction. The authors’ proposed method will give the best results when compared with other existing methods.

The visual tracking of many real-world applications such as robotics, smart monitoring systems, independent driving and human-computer interactions are a major and current research problem in the field of computer vision. This refers to the automated trajectory prediction of an arbitrary target object, often given in the first frame in a bounding box while moving about in successive video frames. In the community of visual tracking or object tracking, DCF has gained more importance. Discriminative trackers strive to train a classifier that differentiates the target item from the background. The fundamental concept is to train a correlation filter that creates high responses around the target and low responses elsewhere. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG and Harris are used for the process of feature extraction. Through experimental analysis, the authors have evaluated several performance assessment metrics such as accuracy, precision, F-measure and specificity. The authors’ proposed method will give the best results when compared with other existing methods.

This process involved DCF which gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique for tracking the objects and these results will be used for identifying the action of the object.

The main theme exists in the process is to identify the tracking motion of the object by using convolution regression with varied features. This method proves that it will provide better results when compared to state of art methods.

This paper seeks to present a comparative study of the traditional usability‐testing process and the re‐engineered usability‐testing process for live multimedia systems.

Provides an overview of current usability‐testing techniques and usability laboratory configurations, and identifies some gaps in the traditional usability‐testing approach.

Traditional usability‐testing procedures are suitable for testing systems in the static environment but prove to be sub‐optimal in testing systems for dynamic (real‐time) environments.

The traditional set‐up is compared with innovative laboratory configuration, which consists of three computer systems: the test system in the middle augmented by two systems on either side that function as the scenario presenter and the data collection system. The re‐engineered usability‐testing process streamlined usability experiments and reduced the task completion times.

The purposes of this paper are to study the characterization of drill bit breakage in printed circuit board (PCB) drilling process based on high-speed video analysis and to provide an important reference for micro drill bit breakage prediction.

Based on PCB drilling experiment, the high-speed camera was used to observe the micro drill breakage process and the chip removal process. The variation of chip in the drilling process was studied and one of the key reasons for the drill bit breakage was analysed. Finally, the swing angles’ feature during the breakage process of the micro drill was analysed and researched with the image processing tools of MATLAB.

The micro drill was prone to breakage mainly because of the blocked chips. The breakage process of the micro drill can be divided into the stage of stable chips evacuation, the stage of blocked chips and the stage of drill bit breakage. The radians of swing angles were basically in the range of ±0.01 when the drilling possess is normal. But when the radians of swing angles considerably exceeded the range of ±0.01, the micro drill bit may be fractured.

This paper presented the method to study the characterization of drill bit breakage in the PCB drilling process by using high-speed video analysis technology. Meanwhile, an effective suggestion about monitoring the radians of swing angles to predict the breakage of micro drill bit was also provided.