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For optical see-through augmented reality (AR), a new method for measuring the perceived three-dimensional location of virtual objects is presented, where participants verbally report a virtual object’s location relative to both a vertical and horizontal grid. The method is tested with a small (1.95 × 1.95 × 1.95 cm) virtual object at distances of 50 to 80 cm, viewed through a Microsoft HoloLens 1st generation AR display. Two experiments examine two different virtual object designs, whether turning in a circle between reported object locations disrupts HoloLens tracking, and whether accuracy errors, including a rightward bias and underestimated depth, might be due to systematic errors that are restricted to a particular display. Turning in a circle did not disrupt HoloLens tracking, and testing with a second display did not suggest systematic errors restricted to a particular display. Instead, the experiments are consistent with the hypothesis that, when looking downwards at a horizontal plane, HoloLens 1st generation displays exhibit a systematic rightward perceptual bias. Precision analysis suggests that the method could measure the perceived location of a virtual object within an accuracy of less than 1 mm.

This paper presents an experimental study on performance of a domestic vapor compression refrigeration system using two different refrigerants, R600a (Isobutane) and R134a (Tetrafluoroethane). Two different compressors were used in this experiment and the input power of both compressors was 270W. In this study, the COP was calculated for each refrigerant at steady state condition. The data taking apparatus were calibrated before conducting the experiment. The result shows, when the refrigerator was equipped with the hydrocarbon (HC) type (designed for R600A) compressor, the COP raised by 12% compared to the hydrofluorocarbon (HFC) type compressor (designed for R134a). According to this comparative analysis and from the environmental, ecological and health points of view, R600a is recommended to use as it is more beneficial than R134a.

In the era of secure communication and the constantly changing pattern of internet applications, traditional packet classification methods fail to achieve the accuracy needed for diverse network management functions. Recently Machine Learning (ML) techniques have been used to design viable packet classification solutions. However, due to the complexity and dynamic feature of internet traffic, efficient packet classification is still challenging for various machine learning algorithms. In this paper, we propose the adoption of feature selection methods through dimensionality reduction to enhance the classifiers' performance. We evaluated the performance of four well-known classifiers, including K-nearest neighbour (KNN), Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) with and without feature selection. We used two feature selection methods, including principal component analysis and Autoencoder. Experimental analysis is performed on real network traffic datasets with binary and multi-class categories. We assessed each classifier's performance using precision, recall, f-score, accuracy, and ROC. Experimental results show that the Precision, Recall, and F-score for the Multi-class problem are improved by 4.7 %, 6%, and 9%, respectively, after adopting either PCA or Autoencoder methods. The classification accuracy is also improved by up to 13%. We can also conclude that Autoencoder performed better for the KNN and LR, while PCA achieved comparable results for both the SVM and DT classifiers.

For optical, see-through augmented reality (AR), a new method for measuring the perceived three-dimensional location of a small virtual object is presented, where participants verbally report the virtual object’s location relative to both a horizontal and vertical grid. The method is tested with a Microsoft HoloLens AR display, and examines two different virtual object designs, whether turning in a circle between reported object locations disrupts HoloLens tracking, and whether accuracy errors found with a HoloLens display might be due to systematic errors that are restricted to that particular display. Turning in a circle did not disrupt HoloLens tracking, and a second HoloLens did not suggest systematic errors restricted to a specific display. The proposed method could measure the perceived location of a virtual object to a precision of ∼1 mm.

Usable x-ray vision has long been a goal in augmented reality research and development. X-ray vision, or the ability to view and understand information presented through an opaque barrier, would be imminently useful across a variety of domains. Unfortunately, however, the effect of x-ray vision on situation awareness, an operator’s understanding of a task or environment, has not been significantly studied. This is an important question; if x-ray vision does not increase situation awareness, of what use is it? Thus, we have developed an x-ray vision system, in order to investigate situation awareness in the context of action space distances.

In this article a graphene based direct-fed multiband planar internal antenna (PIA) for wireless communication is proposed. Graphene, one of the most disruptive materials, is used to enhance the performance of this antenna. Graphene based antennas present noteworthy perfections for the maximum parameters than copper based antennas. Designing and simulating in CST over 0 to 3 THz, it is observed that the PIA indicates the highest efficiency over the different types of graphene based patch antennas. It performs with almost 98 % efficiency.

Diabetes mellitus is an illness that requires continuing medical care and self-management system for the patients to prevent acute complications to reduce the risk of long-term complications. It is a complex disease in which the care for health is supposed to be a satisfactory daily prescribed diet and practicing regular habits, in addition to proper medication and treatment. Good diet and exercise habits, as well as continuous monitoring of blood pressure and sugar are all important for a diabetic patient in order to maintain his proper health. Although there are too many health care solutions exist in the market today, but none of them focus on providing a personalized health care management system for the patient. This proposed mobile application will take an effort to ensure a healthy balanced diet for the patients with the synchronization of balancing food energy. The system provides more facilities by giving the chance to set the medicine alarms, monitoring blood pressure and sugar levels in a chart. The system also provides more safety by making emergency calls and messages in real-time by utilizing GPS reporting technology considering the crucial moment of the patient. Two surveys are included in order to show the necessity of the implementation.

Public Key Cryptography (PKC) based schemes in Wireless Sensor Networks (WSNs) are very difficult to employ due to the resource constraint characteristics of the tiny sensors. Addressing the features of sensor nodes, in this paper, we propose a PKC based architecture, where singular value decomposition (SVD) of pseudo-inverse matrix is used for the key generation. Main attention of our paper is devoted to the security level and power consumption as the Asymmetric Key-Based Architecture is usually considered incompetent in terms of cost efficacy. Our experimental results demonstrate that the proposed scheme ensures better performance in terms of security and power than other prevailing asymmetric schemes considering the MICA2DOT motes of wireless sensor nodes. In addition, it can help to realize the networks accurately, utilizing computational ability, storage and energy level of the current generation sensor nodes.

Information sharing and transmit has been increased exponentially in today's world of Information and Communication Technology. Cryptography is a lasting solution to save the sensitive data from unauthorized access. Finding proper cryptographic structure for wireless sensor networks (WSNs) is extremely challenging as there are some limitations on energy and memory resources of WSNs. Hence, this paper presents an efficient secret key based security architecture which aims to contribute robust security to the topic with ideas to man-in-the-middle attacks and many other hacker situations. In this scheme, singular value decomposition (SVD) of a pseudo-inverse matrix has been used for the key generation. This scheme does not require key handshaking for data transmission and reception between the nodes. The analytical results demonstrate that the proposed scheme shows a significant gain in the rank of security, and is suitable for WSNs of the modern age.

This paper evaluates an automated river level monitoring network. This network contains multiple nodes on which measuring modules are installed. These modules collect raw data and transmit them periodically to a central monitoring system. This monitoring system contains a database that processes the raw data and extracts information. Based on this information, various approximations are made such as water level rise rate, time remaining to exceed the critical level etc. The whole network is implemented as a prototype which yielded satisfactory result.