The form error prediction in side wall machining considering tool deflection

In this research, an effective method for the form error prediction in side wall machining with a flat end mill is suggested. The form error is predicted directly from the tool deflection without surface generation by cutting edge locus with time simulation. The developed model can predict the surface form error accurately about 300 times faster than the previous method. Cutting forces and tool deflection are calculated considering tool geometry, tool setting error and machine tool stiffness. The characteristics and the difference of generated surface shape in up milling and down milling are discussed. The usefulness of the presented method is verified from a set of experiments under various cutting conditions generally used in die and mold manufacturing. This study contributes to real time surface shape estimation and cutting process planning for the improvement of form accuracy.     

Real-time abandoned and stolen object detection based on spatio-temporal features in crowded scenes

Abandoned and stolen object detection is a challenging task due to occlusion, changes in lighting, large perspective distortion, and the similarity in appearance of different people. This paper presents real-time detection methods of abandoned and stolen objects in a complex video. The adaptive background modeling method is applied to stable tracking and the ghost image removing. To detect abandoned and stolen objects, the methods determine spatio-temporal relationship between moving people and suspicious drops. The space first detection method measures the distance between a moving object and a non-moving object in spatial change analysis. The time first detection method conducts temporal change analysis and then spatial change analysis. The potential abandoned object is classified as a definite abandoned or stolen object by two-level detection approach. The time-to-live timer is applied by adjusting several key parameters on each camera and environment. In experiments, we show the experimental results to evaluate our proposed methods using benchmark datasets.     

Page segmentation using minimum homogeneity algorithm and adaptive mathematical morphology

Document layout analysis or page segmentation is the task of decomposing document images into many different regions such as texts, images, separators, and tables. It is still a challenging problem due to the variety of document layouts. In this paper, we propose a novel hybrid method, which includes three main stages to deal with this problem. In the first stage, the text and non-text elements are classified by using minimum homogeneity algorithm. This method is the combination of connected component analysis and multilevel homogeneity structure. Then, in the second stage, a new homogeneity structure is combined with an adaptive mathematical morphology in the text document to get a set of text regions. Besides, on the non-text document, further classification of non-text elements is applied to get separator regions, table regions, image regions, etc. The final stage, in refinement region and noise detection process, all regions both in the text document and non-text document are refined to eliminate noises and get the geometric layout of each region. The proposed method has been tested with the dataset of ICDAR2009 page segmentation competition and many other databases with different languages. The results of these tests showed that our proposed method achieves a higher accuracy compared to other methods. This proves the effectiveness and superiority of our method.     

Effective pedestrian detection using deformable part model based on human model

Recently, pedestrian detection systems have become an important technology in the development of the advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM) that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning technique. Experimental results show that our approach achieves better performance in a detection system than the existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example, the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses a different approach and can easily be applied in real-world scenarios, regardless of occlusions.

     

Paternal and Maternal Information and Communication Technology Usage as Their Very Low Birth Weight Infants Transition Home From the NICU

Little is known about how differently mothers and fathers of very low birth weight (VLBW) infants use information and communication technology (ICT), especially during the vulnerable transition from the neonatal intensive care unit (NICU) to home. This study aims to qualitatively compare and contrast the two groups’ usage of technology. In-depth, semistructured phone interviews were conducted with 25 parents of 16 VLBW infants who had been hospitalized in the NICU. Grounded theory facilitated the understanding of interview data. This article discusses the patterns that emerged around the use of ICT between the two groups and discusses the implications for health information seeking, privacy and misinformation, online social networking, learning technology, choosing a health care provider, and health care communication.     

Enhanced security in internet voting protocol using blind signature and dynamic ballots

In this paper, we introduce an internet voting protocol which satisfies desired security requirements of electronic voting. In the newly proposed protocol, we allow the adversaries to get more power than in any previous works. They can be coercers or vote buyers outside, and corrupted parties inside our system. These adversaries also have ability to collude with each other to ruin the whole system. Our main contribution is to design an internet voting protocol which is unsusceptible to most of sophisticated attacks. We employ the blind signature technique and the dynamic ballots instead of complex cryptographic techniques to preserve privacy in electronic voting. Moreover, we also aim at the practical system by improving the blind signature scheme and removing physical assumptions which have often been used in the previous works.     

Development of a quadruped walking robot AiDIN-III using biologically inspired kinematic analysis

This paper presents a bio-inspired mechanism design for a quadruped walking robot. The approach is derived from the observation on the behaviors of quadruped locomotion, skeletal structure, and the study on the stability of walking based on morphological analysis. In the first, we define the design parameters such as the dimensions of the body and limbs, the center of mass position, and locomotion mechanisms based on surveys on the literatures from biologists. Then, by using the parameters, we propose an useful framework for determining the design parameters of a quadruped walking robot. For implementations, we manufacture a dog-type self-contained quadruped walking robot, named AiDIN-III (Artificial Digitigrade for Natural Environment version III) and the effectiveness of the proposed idea is validated via experimental works.     

Range image denoising using a constrained local Gaussian model for 3D object query service in the smart space

A position and direction is a fundamental information for U-Business as an anywhere service. A mobile device camera image can increase an accuracy of the positioning, and a range image provides significant information in an occlusion scene. U-Business service queries the information with the range image for a precision position or a target object. We present a method for smoothing heavy noisy surfaces acquired by mobile 3D imaging devices to obtain the stable curvature. The smoothing is performed in a way that finds centers of probability distributions, which maximizes the likelihood of observed points with smooth constraints. The smooth constraints are derived from the unit tangent vector equality. This provides a way of obtaining smooth surfaces and stable curvatures. We achieve the smoothing by solving the regularized linear system. The unit tangent vector equality involves consideration of geometric symmetry, and it minimizes the variation of differential values that are a factor of curvatures. The proposed algorithm has two apparent advantages. The first thing is that the surfaces in a scene with various signals-to-noise ratio are smoothed, and then they can earn suitable curvatures. The second is that the proposed method works on heavy noisy surfaces, for example, a stereo camera image. Experiments on range images demonstrate that the proposed method yields the smooth surfaces from the input with various signals-to-noise ratio and the stable curvatures obtained from the smooth surfaces.     

Electromyographic evaluation of a bed assistive device for abdominal surgery patients in postoperative care

OBJECTIVE: Muscle activity with and without the use of commercially available patient assistive devices during bed rising and lowering was quantified. BACKGROUND: Limited research is available in understanding or evaluating the physical benefits of assistive devices for patient use following major abdominal surgery. METHODS: Twenty healthy participants (9 men, 11 women) took part in a laboratory study to test the effects of device configuration (five levels) and bed elevation angle (0 degree and 30 degrees) on mean and peak upper and lower rectus abdominis and external oblique concentric and eccentric muscle activity. RESULTS: Reduced muscle activity was associated with the use of an assistive device, as compared with manual bed rising (unassisted). Positioning the devices at a higher anchor height and/or increasing the bed elevation angle further reduced muscle activity. Objective and subjective differences between the two assistive devices evaluated in the study were found. CONCLUSION: These results suggest that self-assistive devices may speed recovery because of reduced loads on damaged tissues. APPLICATION: Potential applications of this research include the assessment of other commercially available lift aids or comparisons of self-assistive lift aids with hospital-housed lift aids used to speed recovery rates.