Dinh-Mao Bui

Recently, Gaussian process has been excellent for proving the effectiveness in solving the multiuser detection issue in code division multiple access system. Even with limited training sequences, Gaussian process-based solutions still surpass other approaches. However, due to the high complexity in terms of computation, the performance of this approach might be degraded. In this letter, we would like to propose an efficient method to reduce the complexity while still maintaining the desired accuracy. Finally, the proposed method is validated via the experiment.

In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of activity data as their users go about their daily life routine. However, these implementations are device specific and lack the ability to incorporate multimodal data sources. Data accumulated in their usage does not offer rich contextual information that is adequate for providing a holistic view of a user’s lifelog. As a result, making decisions and generating recommendations based on this data are single dimensional. In this paper, we present our Data Curation Framework (DCF) which is device independent and accumulates a user’s sensory data from multimodal data sources in real time. DCF curates the context of this accumulated data over the user’s lifelog. DCF provides rule-based anomaly detection over this context-rich lifelog in real time. To provide computation and persistence over the large volume of sensory data, DCF utilizes the distributed and ubiquitous environment of the cloud platform. DCF has been evaluated for its performance, correctness, ability to detect complex anomalies, and management support for a large volume of sensory data.

Green computing has become one of the hottest trends in recent years. In this research area, the major purpose is to reduce the energy consumption as well as the CO2 emission. Obviously, this topic has been the important issue in the field of electronic and computer engineering. In fact, energy factor might be considered to be a significant cost when running any computing system. Basically, energy savings can be obtained in many parts of the system including memory, peripheral devices, hard disk drive and processor. In processor or CPU level, there is a number of solutions to handle the power consumption. However, most of them based on reactive model which engages the thresholds. Obviously, these techniques are not accuracy and limited to save the power. In this research, a proactive solution based on prediction technique is proposed. Firstly, the utilization of each core of processor is anticipated by using Gaussian process regression. Subsequently, a migration mechanism can be used to migrate the system-level processes between these cores. Finally, the idle cores can be turned off to save the power while still maintaining an acceptable performance.

Recently CCTV-based behavior recognition have gained considerable attention in the transportation surveillance systems to identify normalities, such as traffic jams, accidents, and dangerous driving. An improved method is presented in this paper for the traffic behavior surveillance system by discovering more highly specific features based on the trajectory information. The multiple sparse feature comprising the object location, moving direction, speed, and appearance time length obtained from the moving object detection and tracking stage is modeled by the Pachinko Allocation Model. This hierarchical probabilistic model captures the correlation among the traffic activities and behaviors through the sparse features as the visual words. In the classification phase, the Support Vector Machine constructed from Decision Tree Architecture is utilized. Compared with existing methods, the proposed method outperforms 3-8% approximately in overall classification accuracy.

Biomedical systems have been using ontology matching as a primary technique for heterogeneity resolution. However, the natural intricacy and vastness of biomedical data have compelled biomedical ontologies to become large-scale and complex; consequently, biomedical ontology matching has become a computationally intensive task. Our parallel heterogeneity resolution system, i.e., SPHeRe, is built to cater the performance needs of ontology matching by exploiting the parallelism-enabled multicore nature of today's desktop PC and cloud infrastructure. In this paper, we present the execution and evaluation results of SPHeRe over large-scale biomedical ontologies. We evaluate our system by integrating it with the interoperability engine of a clinical decision support system (CDSS), which generates matching requests for large-scale NCI, FMA, and SNOMED-CT biomedical ontologies. Results demonstrate that our methodology provides an impressive performance speedup of 4.8 and 9.5 times over a quad-core desktop PC and a four virtual machine (VM) cloud platform, respectively.