University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Computer science & Population. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

ChainCluster: Engineering a Cooperative Content Distribution Framework for Highway Vehicular Communications

Abstract: The recent advances in wireless communication techniques have made it possible for fast-moving vehicles to download data from the roadside communications infrastructure [e.g., IEEE 802.11b Access Point (AP)], namely, Drive-thru Internet. However, due to the high mobility, harsh, and intermittent wireless channels, the data download volume of individual vehicle per drive-thru is quite limited, as observed in real-world tests. This would severely restrict the service quality of upper layer applications, such as file download and video streaming. On addressing this issue, in this paper, we propose ChainCluster, a cooperative Drive-thru Internet scheme. ChainCluster selects appropriate vehicles to form a linear cluster on the highway. The cluster members then cooperatively download the same content file, with each member retrieving one portion of the file, from the roadside infrastructure. With cluster members consecutively driving through the roadside infrastructure, the download of a single vehicle is virtually extended to that of a tandem of vehicles, which accordingly enhances the probability of successful file download significantly. With a delicate linear cluster formation scheme proposed and applied, in this paper, we first develop an analytical framework to evaluate the data volume that can be downloaded using cooperative drive-thru. Using simulations, we then verify the performance of ChainCluster and show that our analysis can match the simulations well. Finally, we show that ChainCluster can outperform the typical studied clustering schemes and provide general guidance for cooperative content distribution in highway vehicular communications.

Bayesian Methods for Updating Dynamic Models

Abstract: Model updating of dynamical systems has been attracting much attention because it has a very wide range of applications in aerospace, civil, and mechanical engineering, etc. Many methods were developed and there has been substantial development in Bayesian methods for this purpose in the recent decade. This article introduces some state-of-the-art work. It consists of two main streams of model updating, namely model updating using response time history and model updating using modal measurements. The former one utilizes directly response time histories for the identification of uncertain parameters. In particular, the Bayesian time-domain approach, Bayesian spectral density approach and Bayesian fast Fourier transform approach will be introduced. The latter stream utilizes modal measurements of a dynamical system. The method introduced here does not require a mode matching process that is common in other existing methods. Afterwards, discussion will be given about the relationship among model complexity, data fitting capability and robustness. An application of a 22-story building will be presented. Its acceleration response time histories were recorded during a severe typhoon and they are utilized to identify the fundamental frequency of the building. Furthermore, three methods are used for analysis on this same set of measurements and comparison will be made.

Intracellular signaling pathways of inflammation modulated by dietary flavonoids: The most recent evidence

Abstract: Dietary flavonoids, which occur in many plant foods, are considered as the most active constituents among the plant-derived ones in vitro and in vivo. To date, many studies have addressed the anti-inflammatory activity of flavonoids. However, their considerable structural diversity and in vivo bioavailability make them able to modulate different signaling pathways. The present review attempted to summarize and highlight a broad range of inflammation-associated signaling pathways modulated by flavonoids. Finally, based on the current scientist's literature, structure-activity relationships were concluded. Dietary flavonoids have the ability to attenuate inflammation by targeting different intracellular signaling pathways triggered by NF-κB, AP-1, PPAR, Nrf2, and MAPKs. Identification of the main structural features required for the modulation of these inflammation-related pathways (hydroxylation pattern, C2=C3 double bond) have an important role to play in the development of new anti-inflammatory drugs.

Kinematic analysis and design of a new 3-DOF translational parallel manipulator

Abstract: A new three degrees of freedom (3-DOF) translational parallel manipulator (TPM) with fixed actuators called a 3-PRC TPM is proposed in this paper. The mobility of the manipulator is analyzed via screw theory. The inverse kinematics, forward kinematics, and velocity analysis are performed and the singular and isotropic configurations are identified afterward. Moreover, the mechanism design to eliminate all singularities and generate an isotropic manipulator has been presented. With the variation on architectural parameters, the reachable workspace of the manipulator is generated and compared. Especially, it is illustrated that the manipulator in principle possesses a uniform workspace with a constant hexagon shape cross section. Furthermore, the dexterity characteristics are investigated in the local and global sense, respectively, and some considerations for real machine design have been proposed as well. DOI: 10.1115/1.2198254

Epileptic Seizures Prediction Using Machine Learning Methods

Abstract: Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures' sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects.