Jutao Hao's research while affiliated with University of Shanghai for Science and Technology and other places

Radio frequency identification (RFID) technologies have revolutionized the asset tracking industry, with applications ranging from automated check out to monitoring the medication intakes of elderlies. There are two challenges for the frame-slotted ALOHA algorithms in (RFID). The first challenge is estimating unknown tag-set size accurately; the second challenge is improving the efficiency of the arbitration process so that it uses less time slots to read all tags. A novel anticollision technique is proposed to maximize identification performance in slotted ALOHA-based (RFID) systems. The performances of these protocols are evaluated, and it is verified that this protocols outperform the traditional frame-slotted ALOHA protocols.

Wireless sensor networks are characterized by centralized data gathering, multi-hop communication and many-to-one traffic pattern. These three characteristics may give rise to funneling effects that can lead to severe packet collision, network congestion, packet loss and even congestion collapse. This can also result in hotspots of energy consumption that may cause premature death of sensor nodes and even premature death of entire network. In this paper, exploiting spatial correlation of nodes to form clusters of nodes sensing similar values, and only cluster head sensor reading is transmit to sink, such can efficiently alleviates the funneling effects. A novelty clustering algorithm is proposed which can greatly reduce the number of cluster heads. Experimental results validate the effectiveness of this approach.

The cost-model for XML stream is the theory foundation in the research of XML stream dissemination, fragmentation and evaluation. According to the matching operations in fragmented XML stream system, this paper analyzes the features of the system overhead on client, network and server, and brings in the cost model for fragmented XML stream based on Hole-Filler model. Based on the cost model, this paper illustrates the influence of the fragmentation policy on XML dissemination system by an application and proposes a series of selection policies for XML stream fragmentation to improve the system efficiency, adaptivity and scalability.

In recent years, the number of small size wind farm made up with doubly fed induction generators (DFIG) located within the distribution system is rapidly increasing. DFIG can be utilized as the continuous reactive power source to support system voltage control by taking advantage of their reactive power control capability. In this paper, considering both reactive power control and distribution network reconfiguration can be used to reduce power losses and improve voltage profile, a joint optimization algorithm of combining reactive power control of wind farm and network reconfiguration is proposed to obtain the optimal reactive power output of wind farm and network structure simultaneously. The proposed algorithm has been successfully implemented on the 16 bus distribution network and the results obtained demonstrate the efficiency of the algorithm.

A three-dimensional representation of vasculature can be extremely important in image-guided neu- rosurgery, pre-surgical planning. In this paper, a spatial continuity incorporated multi-attribute fuzzy clustering algorithm (MAFCM S) is proposed to segment entire blood vessels from TOF MRA images. This clustering method takes both the intensity information and the geometrical information into account, while most of the cur- rent clustering methods only deal with the former. In this method, a new dissimilarity measure, which integrates the intensity and the geometry shape dissimilarity, is introduced. Because of the presence of the geometrical information, the new measure is able to differentiate the pixels with similar intensity values within different geometrical shape structures. Experimental results show that the new algorithm can get better segmentation.

This paper presents the particle swarm optimization (PSO) for reactive power and voltage control (Volt/Var Control) in distribution system considering DFIG wind farm. In this paper, the reactive power output of DFIG wind farm has been considered as the control variables in the Volt/Var Control scheme by considering the reactive power compensation capability of DFIG. The minimization of total system real power loss and voltage deviation are utilized as an objective and the PSO is used to determine the reactive power output of DFIG wind farm, tap positions of load tap changer transformers (LTC) and numbers of the capacitor banks. Finally, the proposed scheme is applied to the 33-node distribution system. The results indicate that the proposed Volt/Var control scheme can improve the voltage profiles of distribution networks on a large scale.

Vessel analysis in medical images is important both for diagnostic and intervention planning purposes, especially, a three-dimensional representation of vasculature can be extremely important in image-guided neurosurgery and pre-surgical planning. In this paper, a Bayesian approach is proposed to aggregating geometric shape and intensity features for whole cerebrovascular tree extraction from Time-of-flight Magnetic resonance angiography (TOF-MRA), while most of the current segmentation methods solely deal with the latter. In this method, we first utilize scale space analysis to get shape feature of blood vessels, then both shape and speed features are incorporated into a Bayesian segmentation framework. Maximum a posterior (MAP) method is used to estimate the posterior probabilities of vessel and background for classification. The experimental results show that the proposed method can obtain a better quality of segmentation than those sole feature utilized methods.

In this paper, we present a supervised statistical-based cerebrovascular segmentation method from Time-Of-Flight MRA. The novelty of this method is that rather than model the dataset over the entire intensity range, we at first use a low threshold to eliminate the lowest intensity region, and then use two uniform distributions to model the middle and high intensity regions, respectively. Subsequently, in order to overcome the intensity overlap between subcutaneous fat and arteries, a high order multiscale features based energy function is introduced to enhance the segmentation. Comparing with those sole intensity based segmentation method the newly proposed algorithm can solve the problem of the regional intensity variation of TOF–MRA well and improve the quality of segmentation. The experimental results also show that the proposed method can provide a better quality segmentation than sole intensity information used method.

This paper presents a supervised statistical-based cerebrovascular segmentation method from time-of-flight MRA. The novelty of this method is that rather than model the dataset over the entire intensity range, we at first use a low threshold to eliminate the lowest intensity region, and then use two uniform distributions to model the middle and high intensity regions, respectively. Subsequently, in order to overcome the intensity overlap between subcutaneous fat and arteries, a high order multiscale features based energy function is introduced to enhance the segmentation. Comparing with those sole intensity based segmentation method the newly proposed algorithm can solve the problem of the regional intensity variation of TOF-MRA well and improve the quality of segmentation. The experimental results also show that the proposed method can provide a better quality segmentation than sole intensity information used method.