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The brain–computer interface (BCI) is used to understand brain activities and external bodies with the help of the motor imagery (MI). As of today, the classification results for EEG 4 class BCI competition dataset have been improved to provide better classification accuracy of the brain computer interface systems (BCIs). Based on this observation, a novel quick-response eigenface analysis (QR-EFA) scheme for motor imagery is proposed to improve the classification accuracy for BCIs. Thus, we considered BCI signals in standardized and sharable quick response (QR) image domain; then, we systematically combined EFA and a convolution neural network (CNN) to classify the neuro images. To overcome a non-stationary BCI dataset available and non-ergodic characteristics, we utilized an effective neuro data augmentation in the training phase. For the ultimate improvements in classification performance, QR-EFA maximizes the similarities existing in the domain-, trial-, and subject-wise directions. To validate and verify the proposed scheme, we performed an experiment on the BCI dataset. Specifically, the scheme is intended to provide a higher classification output in classification accuracy performance for the BCI competition 4 dataset 2a (C4D2a_4C) and BCI competition 3 dataset 3a (C3D3a_4C). The experimental results confirm that the newly proposed QR-EFA method outperforms the previous the published results, specifically from 85.4% to 97.87% ± 0.75 for C4D2a_4C and 88.21% ± 6.02 for C3D3a_4C. Therefore, the proposed QR-EFA could be a highly reliable and constructive framework for one of the MI classification solutions for BCI applications.

Nowadays, in a new era of industrial revolution 4.0, there are still many manufacturing companies that tend to neglect the knowledge of optimizing the factory layout. By doing so, the productivity can be increase and lessen the burden of the operator while making sure the quality and also the number of products are also at optimum. The objective of this study is to conduct an analysis on the actual arrangement assembly line using witness software based on QRM in V model assembly and also to propose a new arrangement of the assembly line using Witness software. The data has been collected and recorded through some observation during the visit to X Company. The current layout of the V model assembly line is simulated in the Witness software to get its actual process, percentage of idle, and also the setup time. The software is also used for the new improvement of the assembly line layout to get the comparison for the current layout and after the improvement. Based on the current result, there is some station that has a high percentage of blockage occur in the layout and after the improvement, the percentage of blockage has been reduced which help in increase the production of the model per day. The data that has been recorded from the current is compared with the data after the improvement has been made to the assembly line.

Nurses acknowledge that their understanding of the shortcomings in the utilization of medical devices stems from insufficient knowledge about their correct usage. The use of Quick Response (QR) technology has paved a new gateway for accessing information and resources. The available evidence confirmed the usefulness of QR codes for use in clinical settings as a tool for training. The present research performed a quasi-experimental research design to compare the effect of QR code-based training and traditional methods on the knowledge, skill, and satisfaction of the nursing students in using medical device. For the control group, textual Persian manuals, visual aids in the form of pictures, and URLs were available to the students. For the intervention group, the training approach based on QR codes was explained on the first day of their presence at the heart center. The knowledge, skill and satisfaction evaluations were conducted at the end of the clinical course on day 10. The group of nursing students who used the QR code-based training method had a statistically significant higher mean knowledge and skill score compared to the traditional training method group. The level of satisfaction among students who received training using QR codes was non-significantly higher than traditional method group. Nursing managers and educators can enhance the delivery of high-quality nursing care by using QR code.

Secret sharing is an information security technique where a dealer divides a secret into a collection of shares and distributes these to members of a group. The secret will only be revealed when a predefined number of group members cooperate to recover the secret. The purpose of this study is to investigate a method of distributing shares by embedding them into cover Quick Response (QR) codes in a secure manner using cryptographic keys. The advantage of this approach is that the shares can be disseminated over public channels, as anyone who scans the QR codes will only obtain public information. Only authorized individuals who are in possession of the required keys will be able to recover the shares. This also means that when group members cooperate to recover a secret, the group can determine the presence of an illegitimate participant if the person does not produce a valid share. This study proposes a protocol for accomplishing this and discusses the underlying security of the protocol.

Nowadays, the technologies had experienced massive changes. With the enhancement of technologies, enable devices, software as well as application to be synergised together and creating a new system. The synergised new system enables humankinds to have more productive live ahead in the future. Augmented Reality Mobile Information System (ARMIS) is a system developed using smart phone devices, web studio, mobile application and Quick Response (QR) Code; used to create an overlay image that able to portray the information of products. This system was developed due to the price checker in hypermarket giving out several problems towards the hypermarket customer & retailers, such as giving out a few information regarding towards the product, consuming more electric current, it hidden somewhere in the store and the linear barcode which holding less information. The crucial concept of ARMIS is development of QR Code, development of AR, development of mobile application and the implementation of ARMIS into smart phone devices. Using the model of Alessi Trollip, the product had undergone a few procedures in its development. The main objectives are to develop and introduce ARMIS and to test the ARMIS reliability whether it meets the need of the consumer. ARMIS system also had undergone the Alpha Test where the survey was done towards the The Store hypermarket's customer in Kangar, Perlis, multimedia expert possesses Master or Ph.D and as well as the KPDNKK's officer.

Many methods can be used in manufacturing industries to minimize the wastage. Waste occurred when the output was below the production target and the high rejection rate of the products due to quality issues. One of the reasons is because of the wrong way of working set-up. Inappropriate working environment can cause workers to feel grievances on their limbs with the frequent complaints are fatigue and musculoskeletal. With this complaint, it has an impact on the smooth production process. Many Methods can be used to minimize waste in the manufacturing industry. One of the methods that can be used is Quick Response Manufacturing (QRM) embedded with ergonomics for the work process. Implementation of the QRM and ergonomic methods in manufacturing companies in different countries is still experiencing some constraints. Therefore, this article contains an analysis to identify and evaluate the application of QRM and ergonomics in the Manufacturing industry based on published articles. The approach can be used to assist in formulating the right model in evaluating the strategies for QRM and ergonomic application in the manufacturing sectors.

Reversible watermarking is a technique permitting lossless data hiding. In such a method, the lossless recovering of both watermark and host image is essential. For some applications, such as medical imaging and military systems, it is so vital not only to recover the host image exactly but also to increase security. To obtain these goals, a new reversible watermarking scheme is presented. Since embedding in a transform domain improves security, the proposed method uses Reversible Walsh-Hadamard Transform (RWHT) to commute the host image. Afterward, Singular Value Decomposition (SVD) technique is performed on the transformed image for watermark embedding. For a full recovery, additional information is encoded using Quick Response (QR) code, which is embedded by a prediction-based method. To evaluate the performance of the proposed method, a set of comparative experiments is done. The obtained results confirm the effectiveness of the proposed method in both visual quality and capacity.

Due to fast scanning response and strong damage resistance, Quick Response (QR) code has been used widely in product tracking, item identification, time tracking, document management, and general marketing. The standard QR code consisting of black and white modules, has the following defects: the monotonous color pattern and the poor visual effect. To address these issues, we propose a style transfer method of QR code based on convolutional neural network. Next, we modify the convolutional neural network structure to be in line with the requirements of the style transfer of QR code, which can more preserve the content of the original image than the classic style transfer method. Furthermore, to increase the recognizability of the artistic style QR code, we modify the standard positioning point of the style transfer to generate the QR code. Experiments show that the proposed method has strong robustness.

Operations management researchers have largely studied and analysed lean manufacturing (LM) for its capacity for productivity improvement in industry. Even so, this approach has found several difficulties in dealing with all production types, especially in high-variety and low-volume production. Managers have complemented other approaches to LM to resolve those issues, but there is little research considering quick response manufacturing (QRM). This research shows how QRM can complement LM in a make-to-order environment. Two cases were analysed, where both approaches were used in a complementary way. Data were collected throughout interviews, document analyses and observation. As the main result, we show that QRM complements LM by means of incorporating concepts of system dynamics, exclusively focusing on lead time reduction and the continuous monitoring of utilisation levels.

The vast popularity and useful applications of the QR code were the incentives that encourage the research towards improving its storage capacity and performance. A colored quick response (QR) algorithm is proposed, devised and tested to expand the capacity of the black and white modules to hold colored modules that can fold as many as those available in the 8 bits red-Green-Blue (RGB) color code. Fast Multiplexing Technique (FMT) is established to improve the performance and the storage capacity of the QR color code by multiplexing the black/white QR codes into RGB color shades then folding them into the QR code Modules. Comparative experiments with the classical multiplexing technique -MUX system- proved that FMT has a much better performance, (exponentially faster), while maintaining the capacity multitude to 24 folds that of the classical QR code.