Microscopic traffic flow models are a class of scientific models of vehicular traffic dynamics. Here, we attempted to establish an experimental platform for mimicking microscopic traffic flow models at microscopic dimensions. We achieved this, by monitoring the flow of micro-sized particles transported by the motile cells of living microorganisms. Some researchers have described the cells of protozoan species as “swimming neurons” or “swimming sensory cells” applicable to biological micro-electro-mechanical systems or micro-biorobotics. Therefore these cells, in a controlled environment, may form a good model system for bio-implementable cellular automata for traffic simulation. The living cells of the Paramecium species including those of green paramecia (Paramecium bursaria), actively migrate towards a negatively charged electrode when exposed to an electric field. This type of cellular movement is known as galvanotaxis. P. bursaria was chosen as a model organism since the ideal micro-vehicles required for micro-particle transport must have a particular particle packing capacity within the cells. The present study establishes that the movement of cells with or without the loading of microspheres (Φ, 9.75 μm) can be controlled on a two-dimensional plane under strict electrical controls. Lastly, implementation of microchips equipped with optimally sized micro-flow channels that allow the single-cell traffic of swimming P. bursaria was proposed for further studies and mathematical modeling.
A Fuzzy Inference based Vehicle to Vehicle Network Connectivity Model is proposed to support Optimization Routing Protocol for Vehicular Ad-hoc Network (VANET), where the real-time vehicle to vehicle network connectivity situation of road segments is expressed using fuzzy inference according to the vehicle distribution situation, and the optimized routing protocol modifies the transmission path dynamically and optimizes packet forwarding. The proposed model expresses the real-time vehicle to vehicle network connectivity of each road segment that cannot be easily expressed directly by a mathematical model and decreases the end-to-end delay and the overall network control overhead. The computation time of the proposed protocol is analyzed and shown as O(IlgI+R+V) where I, R, and V represent the number of intersections on a map, the number of road segments on a map, and the number of vehicles within communication range of the vehicle that wants to transfer a data packet, respectively. The simulation tools NS2 and TraNS are used to perform experiments that include wireless data packet transmission and vehicle mobility traces. The results show that the proposed method decreases end-to-end delay and decreases the control overhead by 20% compared with other routing protocols, e.g. GyTAR and RTRP. This proposal implements an intelligent transportation system application and a traffic-monitoring system in NS2 using the optimization routing protocol. This protocol will be implemented to develop a real vehicle telematics system using the embedded system to improve the user-driving experience.
Predicting the behaviors of the stock markets are always an interesting topic for not only financial investors but also scholars and professionals from different fields, because successful prediction can help investors to yield significant profits. Previous researchers have shown the strong correlation between financial news and their impacts to the movements of stock prices. This paper proposes an approach of using time series analysis and text mining techniques to predict daily stock market trends. The research is conducted with the utilization of a database containing stock index prices and news articles collected from Vietnam websites over 3 years from 2010 to 2012. A robust feature selection and a strong machine learning algorithm are able to lift the forecasting accuracy. By combining Linear Support Vector Machine Weight and Support Vector Machine algorithm, this proposed approach can enhance the prediction accuracy significantly above those of related research approaches. The results show that data set represented by 42 features achieves the highest accuracy by using one-against-one Support Vector Machines (up to 75%) and one-against-one method outperforms one-against-all method in almost all case studies.
Mobile devices have emerged as an indispensable part of our daily life, one that has resulted in an increased demand for mobile devices to be able to access the Internet and obtain a variety of network services. However, mobile devices are often constrained by limited storage, huge power consumption, and low processing capability. This paper presents a new computing mode, mobile transparent computing (MTC), which combines ubiquitous mobile networks with transparent computing, to address the above challenges and possibly to enable a new world of ubiquitous operating systems (OSes) and applications with the following characteristics: (1) Mobile devices with no OSes pre-installed are able to load and boot multiple OSes on demand through a transparent network; (2) All resources, including the operating system (OS), applications, and user data, are stored on a transparent server (TS) rather than a mobile terminal, and can be streamed to be executed on mobile devices in small execution blocks; (3) All the personalized services (applications and data) can be synchronized to any other devices with the same user credential. Specifically, we propose a Pre OS technique, which can achieve feature (1) in the MTC model by initializing the mobile device and driving a network interface card (NIC) prior to OS loading, thereby transferring the needed OS streaming block to the mobile device. Experimental results conducted on the tablet demo-board with the model OK6410 based on the ARM11 architecture demonstrate that the Pre OS is able to support remote boot and streaming execution for both Android and Linux OS with satisfactory performance.
Cloud-based video surveillance systems, as a new cloud computing service model, are an emerging research topic, both at home and abroad. Current research is mainly focused on exploring applications of the system. This paper proposes a design and implementation method for cloud-based video surveillance systems using the characteristics of cloud computing, such as parallel computing, large storage space, and easy expandability. The system architecture and function modules are built, and a prototype cloud-based video surveillance system is established in a campus network using key technologies, including virtual machine task access control, video-data distributed storage, and database-active communication methods. Using the system, the user is able to place a webcam in a location that requires monitoring so that video surveillance can be achieved, and video data can be viewed through a browser. The system has the following advantages: low investment and maintenance cost, high portability, easily extendable, superior data security, and excellent sharing. As a private cloud server in the campus network, the system is able to not only provide convenient video surveillance services, but it can also be an excellent practical experimental platform for cloud computing-related research, which carries outstanding application value.
Recently, two-way relay networks have been regarded as a promising technique that can improve bandwidth utilization. In this paper, the power allocation problem for multiuser two-way relay networks with amplify-and-forward protocol is investigated. In order to describe the self-interestedness of nodes in two-way relay networks, a two-level Stackelberg game is introduced to jointly optimize the benefits of the source pair and the relay nodes, where the relay nodes are modeled as leaders and the source pair is modeled as a follower. To facilitate the power allocation process, a distributed game-theoretic power allocation algorithm is proposed. Then, the existence and optimization of the Stackelberg equilibrium for the proposed power allocation algorithm is proven. The convergence of the presented algorithm is also analyzed by proving that price update is a standard function. Simulation results indicate that the proposed power allocation algorithm can improve energy utilization by jointly optimizing the utilities of both source pair and relay nodes.
In this study, we developed a new system for measuring and monitoring the degree of an employee’s fatigue. The system is comprised of an ergometer, a pulsometer, a Kinect sensor, and a computer. It records the heart rate and the 3D motion of a subject during a pedaling exercise. This system is simple, inexpensive, and easy to use. This paper explains the construction of the system, and reports the results of verification experiments with a special focus on the use of the Kinect sensor. The results show that the Kinect sensor is a useful tool for recording body movement and that the system may be used to measure and monitor the degree of fatigue.
A method for understanding the atmosphere is proposed for humans-robots interactions in a multi-agent society, where the individual assessment of the atmosphere is estimated using a Support Vector Regression (SVR) method that represents the emotions of all agents and the atmosphere of the entire society is represented as a fuzzy set in a Fuzzy Atmosfield. This method provides the necessary information that allows each agent (human/robot) in the society to understand the differences between the objective characteristics of the atmosphere and the agent’s individual assessment of the subjective atmosphere and to make appropriate behavioral decisions thereafter. In the experiments, 13 scenarios are tested by four humans. The characteristics of the atmosphere are calculated by applying the proposed method to the emotion data from the four humans. The results are compared with the subjective atmosphere information from the four humans and it is found that the average accuracy reaches 90%. This proposal is planned in order to realize customized services for the humans-robots interactions in a “Multi-Agent Fuzzy Atmosfield,” which is the subject of the authors’ group’s ongoing research project.
This paper considers cooperative formation control on networked multi-agent systems, in which the mobile agents have limited resources. Two event-based strategies are introduced to reduce resource utilization and control actuation in formation control. In one strategy, the event trigger function is designed to use system state, whereas in the other, it is designed to use control input. Theoretical and experimental analyses of the pros and cons of the two strategies are given. In addition, the stabilities of the two event-triggered formation control laws are discussed. The results of simulations conducted confirm the feasibility and effectiveness of the proposed methods.
In E-experiments, the network servers often used to manage experimental plants centrally require high power consumption. In order to reduce such power consumption, cost and size, this paper presents a portable embedded web controller (PEWC) combining embedded technology with Linux + Apache + MySQL + PHP (LAMP), which is the solution stack most widely used globally in Web development. Instead of using servers, the PEWC is used to build a distributed E-experiment system that enables users to do remote control through web pages online. Experimental results show that this system successfully controls robot-arm systems remotely and reduces power consumption by 99%, cost by 90% and size by 95%.
Most previous studies of multi-agent systems only considered the first- and second-order dynamics. In this review, we present the major results and progress related to distributed high-order linear multi-agent coordination. We also discuss the current challenges and propose several promising research directions, as well as open problems that demand further investigation.
With the development of low-level data fusion technology, threat assessment, which is a part of high-level data fusion, is recognized by an increasing numbers of people. However, the method to solve the problem of threat assessment for various kinds of targets and attacks is unknown. Hence, a threat assessment method is proposed in this paper to solve this problem. This method includes tertiary assessments: information classification, reorganization, and summary. In the tertiary assessments model, various threats with multi-class targets and attacks can be comprehensively assessed. A case study with specific algorithms and scenarios is shown to prove the validity and rationality of this method.