Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.
Smart health-care is undergoing rapid transformation from the conventional specialist and hospital-focused style to a distributed patient-focused manner. Several technological developments have encouraged this rapid revolution of health-care vertical. Currently, 4G and other communication standards are used in health-care for smart health-care services and applications. These technologies are crucial for the evolution of future smart health-care services. With the growth in the health-care industry, several applications are expected to produce a massive amount of data in different format and size. Such immense and diverse data needs special treatment concerning the end-to-end delay, bandwidth, latency and other attributes. It is difficult for current communication technologies to fulfil the requirements of highly dynamic and time-sensitive health care applications of the future. Therefore, the 5G networks are being designed and developed to tackle the diverse communication needs of health-care applications in Internet of Things (IoT). 5G assisted smart health-care networks are an amalgamation of IoT devices that require improved network performance and enhanced cellular coverage. Current connectivity solutions for IoT face challenges, such as the support for a massive number of devices, standardisation, energy-efficiency, device density, and security. In this paper, we present a comprehensive review of 5G assisted smart health-care solutions in IoT. We present a structure for smart health-care in 5G by categorizing and classifying existing literature. We also present key requirements for successful deployment of smart health-care systems for certain scenarios in 5G. Finally, we discuss several open issues and research challenges in 5G smart health-care solutions in IoT.
Augmented Reality (AR) systems have been shown to positively affect mental workload and task performance across a broad range of application contexts. Despite the interest in mental workload and the increasing number of studies evaluating AR use, an attempt has yet to be made to identify the relationship between the effects of AR on mental workload and task performance. This paper seeks to address this gap in AR technology literature. With a better understanding how AR affects mental workload and task performance, researchers and developers can design more effective AR systems. 34 articles investigating the effects of the use of AR systems were selected for the review. A positive correlation was found between effects on mental workload and effects on task performance: if the effect on mental workload is positive, then the effects on task performance are more likely to be positive as well, and vice versa. Effectiveness of AR systems were shown to be influenced by the type of AR display device used, relevance and timeliness of content, information presentation, user characteristics and task characteristics. Additionally, the paper addresses the use of the concept of mental workload and limitations in current literature.
As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.
The role of caregivers is very important in the management of person with dementia, where it is not uncommon for them to experience psychological distress. However, the level of distress can be managed and reduced through stra- tegic educational intervention. A systematic review has been conducted through searching Medline, Science direct, Cochrane library and EMBASE databases to provide a narrative synthesis that elaborate on methods and outcomes of the educational intervention among informal caregiver of person with dementia. From a total of 5125 records, eight studies were selected and included in this review, where the results show that educational intervention can be implemented either as individual or group intervention. Group intervention methods mainly focus on training pro- grams such as workshops and lectures, and also group-based discussions. While for individual intervention, most of the activities were implemented through self-learning using technology or computer-based systems. In conclusion, based on the outcome of the studies, both methods of implementations are found to be useful in reducing psycho- logical distress of the informal caregiver.
High requirements imposed by the competitive industrial environment determine the development directions of applied manufacturing methods. 3D printing technology, also known as additive manufacturing (AM), currently being one of the most dynamically developing production methods, is increasingly used in many different areas of industry. Nowadays, apart from the possibility of making prototypes of future products, AM is also used to produce fully functional machine parts, which is known as Rapid Manufacturing and also Rapid Tooling. Rapid Manufacturing refers to the ability of the software automation to rapidly accelerate the manufacturing process, while Rapid Tooling means that a tool is involved in order to accelerate the process. Abrasive processes are widely used in many industries, especially for machining hard and brittle materials such as advanced ceramics. This paper presents a review on advances and trends in contemporary abrasive machining related to the application of innovative 3D printed abrasive tools. Examples of abrasive tools made with the use of currently leading AM methods and their impact on the obtained machining results were indicated. The analyzed research works indicate the great potential and usefulness of the new constructions of the abrasive tools made by incremental technologies. Furthermore, the potential and limitations of currently used 3D printed abrasive tools, as well as the directions of their further development are indicated.
Ultrasound technique is an inexpensive and ecofriendly technology commonly used in oil and gas industry to improve oil recovery and its applications have been successfully tested in both laboratory and field scales. In this technique, high-power ultrasonic waves are utilized downhole to improve oil recovery and reduce formation damage in near wellbore region that causes a reduction in hydrocarbon production rate due to the penetration of mud, scale deposition, etc. In most of the cases, barriers for the oil flow to the wellbore are effectively removed by using the ultrasound technique and the effect of improved oil recovery may last up to several months. The aim of this paper is to provide an overview of recent laboratory, field and mathematical studies to serve as reference for future extensive examination of ultrasound assisted improved oil recovery. As an added value to this field of study, research gaps and opportunities based on the review of recent works were identified and factors that needs to be considered to improve the outcome of future studies were recommended.
An investigation of bending loss characteristics of a Polymer Optical Fiber is presented experimentally. Loss of optical power in an optical fiber due to bending has been investigated for a wavelength of 650 nm. Variations of bending loss with different lengths have been measured, with a number of radii of curvature. Bending Loss equations for short length POF is proposed, which shows the dependence of bending loss on the curvature radii. The equations can be an initial reference or aid in predicting the loss contributes by the polymer optical fiber.
The term "Lock In" as applied to Science and Technology refers to a technology which has been utilised for a certain amount of time and it has been determined that the technology is viable and cost effective. An analysis of the technological advancements in pathology over a period of time shows that the newer technologies in contrast to the older technologies are reaching a state of "Technological Lock In" much faster. Three different discoveries, the development of the autopsy as a research tool, the discovery of the microscope and immunohistochemistry illustrate how rapidly "Technological Lock In" is being achieved with the passage of time. Three probable scenarios are possible because of this rapid "Technological Lock In". Technology may continue to progress at the same pace (an ideal scenario), may plateau until pathologists accept and absorb new technologies or thirdly, develop very rapidly so that the technology may never reach pathology practice. What will the future be? How will technology influence the principles and practices of Pathology? Only time will tell.
This research article investigates the effect of organisational climate and technology usage on employees' physiological and emotional health damage resulting from face-to-face bullying and cyberbullying at the workplace. Furthermore, we investigated emotional intelligence as a coping strategy to moderate employee physiological and emotional health damage. The research used a quantitative research design. A five-point Likert-scale questionnaire was used to collect data from a multistage sample of 500 officials from Pakistan's four service sectors. Results revealed that organisational climate and technology usage are negatively related to face-to-face bullying and cyberbullying at the workplace. At the same time, workplace bullying adversely affects an employee's emotional and physiological health. However, emotional intelligence can reduce an employee's emotional health damage due to workplace bullying. Thus, we suggest incorporating emotional intelligence training at the workplace to minimise the devastating effects of face-to-face bullying and cyberbullying on employees' physical and emotional health.
Conventional and license-free radio-controlled drone activities are limited to a line-of-sight (LoS) operational range. One of the alternatives to operate the drones beyond the visual line-of-sight (BVLoS) range is replacing the drone wireless communications system from the conventional industrial, scientific, and medical (ISM) radio band to a licensed cellular-connected system. The Long Term Evolution (LTE) technology that has been established for the terrestrial area allows command-and-control and payload communications between drone and ground station in real-time. However, with increasing height above the ground, the radio environment changes, and utilizing terrestrial cellular networks for drone communications may face new challenges. In this regard, this paper aims to develop an LTE-based control system prototype for low altitude small drones and investigate the feasibility and performance of drone cellular connectivity at different altitudes with measuring parameters such as latency, handover, and signal strength. The measurement results have shown that by increasing flight height from ground to 170 m the received signal power and the signal quality levels were reduced by 20 dBm and 10 dB respectively, the downlink data rate decreased to 70%, and latency increased up to 94 ms. It is concluded that although the existing LTE network can provide a minimum requirement for drone cellular connectivity, further improvements are still needed to enhance aerial coverage, eliminate interference, and reduce network latency.
Biosensors operating based on electrical methods are being accelerated toward rapid and efficient detection that improve the performance of the device. Continuous study in nano- and material-sciences has led to the inflection with properties of nanomaterials that fit the trend parallel to the biosensor evolution. Advancements in technology that focuses on nano-hybrid are being used to develop biosensors with better detection strategies. In this sense, titanium dioxide (TiO2) nanomaterials have attracted extensive interest in the construction of electrical biosensors. The formation of TiO2 nano-hybrid as an electrical transducing material has revealed good results with high performance. The modification of the sensing portion with a combination (nano-hybrid form) of nanomaterials has produced excellent sensors in terms of stability, reproducibility, and enhanced sensitivity. This review highlights recent research advancements with functional TiO2 nano-hybrid materials, and their victorious story in the construction of electrical biosensors are discussed. Future research directions with commercialization of these devices and their extensive utilizations are also discussed.
The IEEE 802.11ah standard relies on the conventional distributed coordination function (DCF) as a backoff selection method. The DCF is utilized in the contention-based period of the newly introduced medium access control (MAC) mechanism, namely restricted access window (RAW). Despite various advantages of RAW, DCF still utilizes the legacy binary exponential backoff (BEB) algorithm, which suffers from a crucial disadvantage of being prone to high probability of collisions with high number of contending stations. To mitigate this issue, this paper investigates the possibility of replacing the existing exponential sequence (i.e., as in BEB) with a better pseudorandom sequence of integers. In particular, a new backoff algorithm, namely Pseudorandom Sequence Contention Algorithm (PRSCA) is proposed to update the CW size and minimize the collision probability. In addition, the proposed PRSCA incorporates a different approach of CW freezing mechanism and backoff stage reset process. An analytical model is derived for the proposed PRSCA and presented through a discrete 2-D Markov chain model. Performance evaluation demonstrates the efficiency of the proposed PRSCA in reducing collision probability and improving saturation throughput, network throughput, and access delay performance.
The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.
Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems' scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.
In the modern age, digital games are widely used as informal media for Science, Technology, Engineering, and Mathematics (STEM) education and medical therapy for game-based learning. Digital games provide learners with a graphical system of interaction that enhances scientific concepts within an enjoyable environment. The vastly increasing number of digital games produced in the market affects the quality of STEM digital games while requiring multidisciplinary expertise. This paper proposes a framework for STEM digital game-based learning encompassing input-process-output stages. Several studies from the early 2000s onward were reviewed to discuss and present a new perspective on a framework for the design and development of digital games, particularly for STEM. This proposed framework consists of digital game development as input, experience as a process, and constructs as output. This simple and precise framework will generate a universal product for various types of learners. It can thus be used as a guideline for game designers, developers, and experts to develop STEM digital games and achieve better learning outcomes.
Taxis, being a significant element of mobility, play a sufficiently great role in public transportation. However, the issues towards taxi services have gradually increaseddue tothe poor servicesgiven to passengers by taxi operators,leadingto the demand for otherride servicealternatives.With the developmentof technology, GrabCarwasdeveloped and launched asanonline taxi booking through smartphone application which can easily connectpassengers and drivers. The main purpose of this study is to find out the perception of taxi operators towards GrabCar service and vice versa sothatthere will be a clear point of view to identify opportunities andchallenges for taxi operators. Standardized open-endedinterviews were conducted with5taxi and 5 GrabCar operatorsin Kuala Terengganu and Kuala Nerus. Interview transcriptswere analyzed using content analysis. The findingsshowedthat there weremore challenges than opportunities for the taxi operators. This studycould help the government to improvetaxi services in general and GrabCar servicesin sharing economy.
Technology is a tool that influences today’s lifestyle especially in the employment sector. The use of technology in employment may help in increasing the worker’s appraisal. The efficiency can be measured based on the increase of the productivity and the acceptance in using this system to help in upgrading the quality of the product output. It also assists in communication, increasing the worker’s integrity, productivity and the spread of information.
Assessment of instructors by students is needed for assessing the teaching quality of a lecturer towards achieving the objectives of a course. This paper aims to examine the techniques used in sentiment analysis for assessing the effectiveness of a lecturer’s or a teacher’s teaching style in the learning process at a university or school. In addition, the effectiveness of sentiment analysis techniques in assisting the teaching evaluation process is also discussed. The challenges for assessing the quality of teaching of National Defence University of Malaysia (UPNM) lecturers are also discussed in this paper. The sentiment analysis technology is capable of analysing views or opinions on a matter, regardless of whether they are positive or negative. Data from the sentiment analysis can be used by specific parties or anyone else to rectify any weakness or to improve any aspect that the user commented on. The purpose of this study is not to find the weakness of the lecturer, but rather the results of this assessment process can be useful to the management for rectifying weaknesses and for improving the teaching process.
Microalgae have drawn significant interest worldwide, owing to their enormous application potential in the green energy, biopharmaceutical, and nutraceutical industries. Many studies have proved and stated the potential of microalgae in the area of biofuel which is economically effective and environmentally friendly. Besides the commercial value, the potential of microalgae in environmental protection has also been investigated. Microalgae-based process is one of the most effective way to treat heavy metal pollution, compared to conventional methods, it does not release any toxic waste or harmful gases, and the aquatic organism will not receive any harmful effects. The potential dual role of microalge in phytoremedation and energy production has made it widely explored for its capability. The interest of microalgae in various application has motivated a new focus in green technologies. Considering the rapid population growth with the continuous increase on the global demand and the application of biomass in diverse field, significant upgrades have been performed to accommodate green technological advancement. In the past decade, noteworthy advancement has been made on the technology involving the diverse application of microalgae biomass. This review aims to explore on the application of microalgae and the development of green technology in various application for microalgae biomass. There is great prospects for researchers in this field to delve into other potential utilization of microalgae biomass not only for bioremediation process but also to generate revenues from microalgae by incorporating clean and green technology for long-term sustainability and environmental benefits.