Scientia Iranica
Volume:26 Issue: 1, Jan-Feb 2019

Model-mediated teleoperation is a predictive control approach for controlling haptic teleoperation systems whereby the environment force is virtually located on master side in order to increase the stability and transparency of the system. This promising approach, however, results in new challenges. One pivotal challenge is the model jump effect, which stems from the delay in correct creation of the virtual environment. Previous works have endeavored to reduce this effect; however, they either led to transparency decrease or assumed simplified environment models. In this paper, we propose a control approach for this aim based on the idea of decoupling. This means that when a new environment has been identified, the operation is interrupted and no signal is transmitted between master and slave sides. During this time, both sides are controlled by their own sliding mode controllers until the system reaches stability. The main advantage of this method is its independence from environment type, which makes it usable for different kinds of applications. To verify the effectiveness of the proposed approach simulation tests are conducted. The results show the system is stable in interaction with hard and soft environments in presence of large time delays in communication channels.

Design process, due to its information- and innovation-intensive nature, is highly susceptible to change, thus waste. This attracted the attention of lean design/construction professionals in the past few years. However, limited, if any, researches have addressed this issue from the human behavior perspective. This research proposes a method that exploits the potential of the Last PlannerÒ System (LPS) in design management. The main contribution of this paper is improving the applicability of the LPS to design processes by incorporating a gamified pay-for-performance system to the normal practice of the LPS. It encourages motivating design engineers by granting them single point, autonomous responsibility to perform their tasks. To this end, the proposed method shifts the focus of design managers away from predicting the workflow and chronologies of design tasks towards motivating design engineers to eliminate non-value-adding works/time. To bolster the concept and examine the method, it was put into practice by construction design teams. Findings corroborate the efficiency of the method in eliminating the non-value-adding works from design processes. The findings are of practical value to consulting firms, especially design team managers who seek to maximize innovation, competency and quality outcome.

In this paper, we propose a novel approach for knowledge discovery from textual data. The generated knowledge base can be used as one of the main components in the cognitive process of question answering systems. The proposed model automatically extract relations between named enti- ties in Persian. Our proposed model is a bootstrapping approach based on n-gram model to nd the representative textual patterns of relations as n-grams in order to extract new knowledge about given named entities. The main motivation for this work is the characteristic of the sentence structure in Persian which, in contrary to English sentences, is in subject- object-verb format. The proposed approach is a purely statistical one and no background knowledge of the target language is required. This makes our method applicable to any open domain relation extraction task. How- ever, as for our test-bed, we focus on the domain of biographical data of international poets and scientists to build a knowledge base about them. Qualitative evaluations based on human assessment is an evidence for the ecacy of our method.

Utilizing a humanoid social robot to systematically teach music to children with autism has not received wide attention to date. In this study, a novel robot-assisted music-based scenario has been designed in order to: 1) teach fundamentals of music via a xylophone-/drum-player robot as a teacher assistant, and 2) improve social/cognitive skills through active music games in children with autism. The educational-therapeutic interventions were conducted in an eleven-session case study program on three high-functioning and one low-functioning children with autism taking into consideration the children’s, parents’, and therapists’ experience during the program. The results indicated that as a tool and facilitator, the NAO robot does have the ability to teach musical notes/rhythms to the participants with high-functioning autism. It was also observed that the severity of the participants’ autism as well as the stress of the parents decreased somewhat during these sessions. Furthermore, noticeable improvements were seen in social/cognitive skills of all four participants; as well as the positive effect of this program on fine motor imitation skills of two subjects after the interventions. The progress reported from this preliminary exploratory study confirmed the potential benefits of using social robots and intelligent technologies as a facilitator in music-teaching and cognitive-rehabilitation.

In recent years, with the advent of digital imaging technology such as color printers and color scanners, it has become easier for counterfeiters to produce fake banknotes. The spread of counterfeit money causes loss to everyone involved in financial transactions. Therefore, an effective and reliable verification technique is necessary for successful and reliable financial transactions. This paper presents a cognitive computation based technique for paper currency verification. In this regard, Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD) analysis of counterfeit and genuine banknotes are performed. This experimentation confirmed that, material used in preparation of genuine and counterfeit banknotes is totally different from each other. Based on these findings, a set of discriminative and robust features is proposed to reflect these differences in currency images. The proposed features represent the material of the banknote such as printing ink, chemical composition, and surface coarseness of the banknotes. With these robust features, Support Vector Machines (SVMs) is employed for classification. In order to evaluate the performance of proposed technique, experimentations are performed on a self-constructed dataset of Pakistani banknotes, comprised of 195 currency images, including 35 counterfeit banknotes. The results confirm that proposed system achieves 98.57% verification ability on properly captured images.

Brain Computer Interface (BCI) systems, which are based on motor imagery, enable human to command artificial peripherals by merely thinking to the task. There is a tremendous interest in implementing BCIs on portable platforms, such as Field Programmable Gate Arrays (FPGAs) due to their low-cost, low-power and portability characteristics. This article presents the design and implementation of a Brain Computer Interface (BCI) system based on motor imagery on a Virtex-6 FPGA. In order to design an accurate algorithm, the proposed method avails statistical learning methods such as Mutual Information (MI), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). It also uses Separable Common Spatio Spectral Pattern (SCSSP) method in order to extract features. Simulation results prove achieved performances of 73.54% for BCI Competition III-dataset V, 67.2% for BCI Competition IV-dataset 2a with all four classes, 80.55% for BCI Competition IV-dataset 2a with the first two classes, and 81.9% for captured signals. Moreover, the final reported hardware resources determine its efficiency as a result of using retiming and folding techniques from the VLSI architecture perspective. The complete proposed BCI system not only achieves excellent recognition accuracy but also remarkable implementation efficiency in terms of portability, power, time, and cost.

A total of 120 commercial drivers from four age groups participated in the expressway driving test in Shandong, China to perform 2, 3, and 4 h continuous driving tasks and collect the data on the driver's blinking, driving performance and self-reported level of sleepiness. Two-way repeated measures ANOVA was used to evaluate the effects of driving duration on the variation of the eye-blink behavior, driving performance and subjective feeling of sleepiness across the different age groups over the time periods tested. Additionally, Pearson product-moment correlation was used to quantify the association between the variations of the dependent variables. The results showed that there was significant difference between groups, significant effect over time and significant interaction between the age and driving duration in the variations of the driver’s blink frequency, blink duration, closure duration, speed perception, choice reaction time, number of incorrect action judgments and subjective level of sleepiness. However, a significant difference varied over time, but no effect of the interaction between groups and time were found in the variation of the driver’s attention allocation value. Furthermore, driver’s eye blink measures were more sensitive to sleepiness and older drivers were more likely to get sleepy in long distance driving.

The technological achievements in digital publishing have made paperless education possible even in K-12 education. Aside from high bandwidth distribution infrastructure, main problems of digital publishing are preserving personal information and protecting the rights of copyrighted contents. Although, specially designed digital rights management (DRM) systems can be used to control distribution and usage of private and/or copyrighted contents in K-12 education, dealing with large number of bursty concurrent access requests and changing the access rights of large amount of students from one content class to another at the end of each education period makes the problem different from existing ones. This paper introduces a new DRM system, called EDU-DRM, that includes a novel bit based authorization approach that reduces the processing time for authorization requests and automatize the access right adjustments with predefined rules for K-12 education. During the study, an experimental framework is designed using Apache Bench to analyze the proposed approach and its evaluation. The system is compared with XML based authorization approach and the results are presented in the paper.

One of the most interesting topics in the field of human machine interaction is workload. In this paper, using information theory concepts, baud rates generated in all subsystems of a generic simulator of piloting tasks were calculated and then, a unique numerical index presenting an estimation of overall workload was extracted. To examine the effectiveness of offered criteria, three tests with different levels of autopilot failure were designed in which existing workload were labeled based on involving baud rates. A group of subjects performed these tests as the pilots while recording their own idea about perceived workload. Results confirmed that there were statistically significant differences between the averages of scores assigned by subjects to the overall workload for three levels of difficulty. Consequently, the proposed quantitative index is effective enough for determination of workload levels in the simulator environment and facilitates creation of needed scenario noticeably.

Information Technology (IT) usage in university campuses consume enormous amount of power and incur other costs in cooling and related operations. Currently, universities are infusing environmental friendly initiatives in their campus. However, IT professionals in most universities are still not sure how to infuse and assimilate environmental friendly practices. Hence, Green Information Systems (IS) is suggested as the utilization of technologies and systems for a Greener and more sustainable planet. Accordingly, this study carried out a review on existing Green IS practices and further proposes a decision support framework based on Case Based Reasoning (CBR) technique. The proposed framework utilizes CBR to support the decision making of IT professionals in infusing and assimilating Green IS within university campuses. Findings from this study show how the framework utilizes CBR to support IT professionals in assimilating Green IS lifecycle and infusing Green IS determinants. Furthermore, findings from this study show how IT professionals can Green their university campuses. Moreover, this research study would also be of interest to data and knowledge management community as well as environmental scientists and sustainability researchers.

Educational institutes are adopting Online Social Networks (OSNs) to facilitate learning activities in university campuses. However, before implementation a new technology there is need to identify and measure the factors that influence users' acceptance of the new technology. This will aid to critically predict the success or failure of the new technology that is to be adopted. Therefore, this study aims to identify the factors that influence student’s acceptance of online social networks as learning tool. Besides, a research model is developed based on the identified factors grounded on Technology Acceptance Model (TAM). In addition, Structural Equation Model (SEM) was employed to validate the developed model hypotheses based data collected by employing online survey from 537 students in University Malaysia Pahang (UMP). Furthermore, findings from this study have significant implications and considerable value for higher educational institutes to devise better strategies for adoption of OSNs' as a learning tool.