Recently, several Internet of Things (IoT) service platforms have been proposed to facilitate IoT application deployment. These platforms typically utilize the lightweight MQTT or CoAP application protocol, optimized for massive IoT applications. Unfortunately, these protocols are not suitable for the emerging, more sophisticated Artificial Intelligence of Things (AIoT). Session initiation protocol (SIP), in contrast, is a signaling and controlling protocol for real-time multimedia sessions, and has been viewed as a better candidate to provide a full range support of different broadband, critical, and industrial AIoT applications. However, there exists no generic SIP-based AIoT service platform that supports creations and operations of heterogeneous AIoT applications with various quality of service. This article presents the first SIP-based AIoT service platform, AIoTtalk, that enables rapid development of scalar and multimedia AIoT applications. Moreover, we deploy an experimental testbed and two real SIP-based AIoT applications to demonstrate the applicability and the performance of our AIoTtalk under both the cloud and edge scenarios. The experimental results show that, together with accurate model predictions and edge-virtualization auto scaling, AIoTtalk guarantees low latency and high quality of experience for messaging and streaming-based AIoT applications.

Unmanned aerial vehicles (UAVs) equipped with imaging and ranging sensors have become an effective remote sensing data acquisition tool for digital agriculture. Among potential products derived from UAVs, high-resolution orthophotos play an important role in several phenotyping activities, such as canopy cover estimation and flowering date identification. Current structure from motion (SfM) tools for image-based 3-D reconstruction and orthophoto generation cannot perform well when working with large-scale imagery over mechanized agricultural fields. This failure is mainly due to their inability to identify enough conjugate points among overlapping images captured at low altitudes. This study addresses such limitation through a new strategy that uses plant row segments as linear features in the triangulation process. The linear features are derived in two steps. First, an automated approach is implemented to extract plant row segments from the LiDAR data which are then back-projected to the imagery using available trajectory and system calibration parameters. In the second step, a machine-assisted strategy is used to adjust the line segments in image space for deriving accurate linear features. In the proposed framework, the triangulation process is conducted by investigating two mathematical models—referred to as object-space and image-space coplanarity constraints—for incorporating linear features in the bundle adjustment (BA). The orthophoto is generated using the refined trajectory and system calibration parameters derived from the BA process. Several experimental results over an agricultural filed show that the proposed framework outperforms commonly used SfM tools, e.g., Pix4D Mapper Pro and Agisoft Metashape in terms of generating orthophotos with high visual quality and geolocation accuracy. Also, results indicate that the object-space coplanarity constraint is more robust against potential noise in line measurements when compared to the image-space coplanarity model. However, both models lead to high absolute accuracy in the range of ±2–4 cm when the noise level in the image measurements of points along the line is reasonable, i.e., ~5–10 pixels.

To guarantee low-latency and reliable end-to-end connectivity and provide suitable resources for vehicle-to-everything (V2X) services, the network function virtualization (NFV)-based multi-access edge computing (MEC) technology has been recognized as an effective solution. In the literature, few studies have proposed corresponding NFV-based MEC frameworks to provide V2X services. However, these works only discuss the concept of applying the NFV to the MEC to offer V2X services without implementing it in a real testbed. On the other hand, within the NFV-based MEC framework, several studies have investigated the NFV auto scaling mechanism to provide suitable resources for different services. However, these works are not suitable for V2X services, which exhibit unique traffic characteristics. In this paper, we (1) implement an NFV-based edge platform to provide vehicles with low-latency V2X services, (2) implement a vehicle mobility-driven NFV auto scaling mechanism that forecasts the resource requirements for V2X services and triggers the scaling operations if needed, and (3) use the open source software to build an NFV testbed and deploy our platform as virtual network functions to the NFV testbed. Finally, we implement a simple V2X service to validate our proposed edge platform, justifying that it, together with our NFV auto scaling mechanism, requires affordable processing delay and supports low V2X service latency.

Internet of things (IoT) technologies have been applied to realize various applications/services, ranging from massive, broadband, critical to industrial automation IoT applications. Recently, several IoT service platforms have been proposed to facilitate IoT application deployment, such as OpenMTC and IoTtalk. These platforms typically utilize the lightweight MQTT or CoAP application protocol to communicate with their served devices, both optimized for massive IoT applications within constrained networks. Unfortunately, these protocols are not applicable to those more advanced IoT applications, which demand high data rate and low latency. To provide a full range of supports for different IoT application scenarios, Session Initiation Protocol (SIP) can be a better candidate, which, besides instant messaging, can also handle the long session semantic and the publish-subscribe semantic. In the literature, different SIP semantics have been utilized to implement different IoT applications/systems. However, to the best of our knowledge, there exists no generic SIP-based IoT service platform that integrates relevant capabilities for developers to flexibly develop/deploy heterogeneous IoT applications, demanding different quality of service. This paper implements a SIP-based IoT service platform, iSIPtalk, which enables rapid development of more advanced IoT applications. To justify the applicability of iSIPtalk, we deploy a real testbed and implement a vehicular service within our proposed iSIPtalk. Finally, via delay measurements using the real testbed and the vehicular service, we demonstrate the performance of our iSIPtalk.

Lane width evaluation is one of the crucial aspects in road safety inspection, especially in work zones where a narrow lane width can result in a reduced roadway capacity and also, increase the probability of severe accidents. Using mobile mapping systems (MMS) equipped with laser scanners is a safe and cost-effective method for rapidly collecting detailed information along road surface. This paper presents an approach to derive lane width estimates using point clouds acquired from a geometrically-calibrated mobile mapping system. Starting from an accurate LiDAR point cloud, the road surface is extracted with the assistance of trajectory elevation data. Lane markings are identified based on the intensity data. Next, the lane marking centerline is derived and clustered to identify areas with ambiguous or missing lane markings and finally, use the normal (or, unambiguous) lane markings to estimate the lane width. The derived lane width estimates are used to develop a reporting mechanism for areas with narrow lanes, ambiguous lane markings, missing lane markings, and/or wide lanes.

Indoor positioning is one of the key technologies for enabling location-aware applications. To the best of our knowledge, existing indoor positioning technologies are aimed at providing very high-accuracy location information, but are hindered by the need of a infrastructure with high installation and maintenance costs. In addition, their accuracy is usually highly variable and vulnerable to environmental changes or external interference. Thus, for applications of indoor human tracking, the practical considerations of choosing a suitable indoor positioning technique should be to provide adequate accuracy while also being economical. Based on this observation, our study presents an indoor, room-level tracking method characterized by an infrastructure with low costs to build and maintain. The user carries a mobile device that can determine his/her location with the help of Bluetooth beacons installed at the intersections between rooms and hallways. In contrast to traditional radio-fingerprint map and proximity approaches, in this study the mobile device tracks the user's moving direction and the temporal variation of the radio signal strength from nearby Bluetooth beacons, to determine when the device enters or leaves a given room or hallway. Therefore, by determining the sequence of a user's movements, his/her moving path can be recorded for indoor tracking or navigation.

Because of real-time requirements and low hardware-cost constraints, conventional TV scalers can only employ basic interpolation technique and thus introduces some artifacts that degrade the viewing quality of the output sequences. In this paper, a low-complexity super-resolution (SR) algorithm, which can provide vivid output image with rich details and sharp edges, and its associated hardware architecture, is proposed. There are two main contributions in this paper. The first is the development of the database-free texture synthesis technique. With the fractal property of nature images, it is possible to find proper high-resolution patches in a low-resolution input image itself. Therefore, the texture synthesis can be performed without database to provide proper and rich details. In addition, a faithful reconstruction constraint is used to maintain the temporal consistency. The second contribution is the hardware architecture design of the database-free texture synthesis. Partial-sum reuse technique is developed to reduce 76% of computation in the texture synthesis, and a tile-based processing technique is proposed to dramatically reduce the on-chip memory and off-chip memory bandwidth requirements. Experimental results show that the proposed SR algorithm outperforms other ones with reasonable hardware cost, where 766k in gate count and 22 kB in on-chip SRAM are required to achieve full high-definition processing ability at the working frequency of 240 MHz. The results show that the proposed algorithm and architecture are able to provide high-quality output in real-time while solving the problems of zigzag and blurred effects caused by the conventional scalers.

In a video-based surveillance system, a mobile camera can provide dynamical and wider monitoring range and the video data transmitted from cooperative mobile cameras can be used to actively detect the objects of interest. However, it is a difficult task to accurately detect the moving objects from the image frames captured by the mobile cameras and the data flow of surveillance video from multiple cameras could be huge. The camera motion usually causes the shifting of static background as well as the moving objects in the captured image frames. In order to correctly estimate the motion of moving objects, a voting-based motion estimation algorithm is proposed to process the image frames captured by the mobile camera. Based on the estimation, a content-based video transmission mechanism is then implemented to further effectively decrease encoding cost and bandwidth utilization. The overall approach consists of voting-based motion estimation, moving object edges detection and content-based sampling coding at temporal and spatial scales. Without knowing the prior knowledge of camera motion, the motion estimation algorithm only utilizes the shifting information of edges of static background to estimate the camera movement. The shifting information is determined based on the voting decision of several representative regions of interest and the estimated motion is then used to compensate for the visual content obtained from the captured image frames. The proposed algorithms have been experimentally tested on several practical scenarios and it is demonstrated that, under limited network bandwidth, the transmitted image quality can be progressively achieved and the transmission bandwidth utilization can be effectively decreased.

How to motivate students to learn and improve the efficiency of interactive teaching is a challenging issue. The purpose of this study is to develop a new game-based learning system using toy robots to enhance students' learning motivation and effectiveness. The new learning system integrated wireless communication technologies, digital multimedia contents, world map, many attractive tri-dimensional toy models and toy robots, etc. Two learning modes are designed to assist the teacher in teaching and the students can perform a self or active learning. With the help of our new game-based learning system, the students are motivated to learn knowledge as though they are playing the game with toy robots. As a result, the students' learning quality is significantly improved.

The booming of information technology has influenced the evolution of learning patterns and the integration of information technology into teaching has became one of the mainstream methods. The teaching and delivery of knowledge are no longer restricted to traditional lectures. As Learning Management System (LMS) at the present stage can accommodate a diversity of learning requirements and functions, there are many literatures to study on the application of the LMS. However, as the development of relevant industries and technologies, users’ requirements on the LMS may be influenced and changed. How will these changes affect the development of the LMS? What are the changes and developments of the LMS required for addressing the current and future requirements of the users? To address these questions this study aims to perform a comprehensive investigation on the technologies, applications, and analysis of the LMS to realize the developments of the LMS.

Double interpolation quality evaluation can be used as a measurement of an interpolation operation. By using this double interpolation framework, a high efficient direction-adaptive upsampling algorithm is proposed without any threshold setting and post-processing. With the proposed upsampling algorithm, the problem of zigzagging artifacts on the edge no longer exists. Moreover, the proposed algorithm has low computation complexity. The experimental results show that the proposed algorithm has high quality image output.

Although the approaches about how to manifest the technology innovation could contribute to the development of innovative learning technologies, the neglect of teachers’ individual differences in technology applications may interrupt the technology diffusion. In this paper two case studies about teaching with innovative learning technologies (including mobile learning environment and interactive response system) are discussed to show how teachers' characteristics influence their teaching with technology and to display a useful way to help teachers apply the technology in their best way. We expect that considering teachers skills, and attitudes while introducing new technologies for learning will provide wider acceptance of the technology implementation in education.

In order to explore why some learners have better learning effects with the simulation tool, and the others do not, the researchers used the quasi-experimental design and think-aloud method to investigate the relations between individual differences and learning effects from students’ learning processes with the simulation tool, Dynamic Simulation Assisted Statistical Learning (DSASL). The results show that the DSASL can correct the students’ misconceptions and enhance their concept understandings. However, there are individual differences in students’ learning effects when learning with DSASL. Finally, the researchers analyze the transcripts of think-aloud and find that the learning process may influence the students' learning effects.

In recent years, 3D virtual reality technology has been growing fast. The user interface of the Internet is facing a new challenge. Many organizations have begun to propose various kinds of specifications and standards for Web3D. The specification draft of VRML1.0 was proposed in 1994. In 2000, Java3D, Extensible 3D (X3D) and MPEG-4 Binary Format for Scene (BIFS) were formally included as important components of Web3D development by the Web3D Consortium. Quickly developing information science and technology have been shortening the half-life of knowledge. The time and the cost have become important factors in developing programs and software. Among all kinds of platforms and compiling devices, the portable bytecode of Java can reduce the waste caused by different platforms. Java technology is growing up, and its compression and security technology have improved in recent years. All of these aspects serve in making Java extremely competitive in the future. This research uses Java and Java3D API to develop a Web3D virtual reality learning environment, and proposes the development of techniques and praxis from four aspects; the virtual scene construction, learning history record, file compression technology, and security. Designers will be able to use this particular VR as a reference for developing Web3D virtual reality in the future.