Cheng-Long Chuang

A Distributed RSS-Based Localization Using a Dynamic Circle Expanding Mechanism

This paper focuses on localization that serves as a smart service. Among the primary services provided by Internet of Things (IoT), localization offers automatically discoverable services. Knowledge relating to an objects position, especially when combined with other information collected from sensors and shared with other smart objects, allows us to develop intelligent systems to fast respond to changes in an environment. Today, wireless sensor networks (WSNs) have become a critical technology for various kinds of smart environments through which different kinds of devices can connect with each other coinciding with the principles of IoT. Among various WSN techniques designed for positioning an unknown node, the trilateration approach based on the received signal strength is the most suitable for localization due to its implementation simplicity and low hardware requirement. However, its performance is susceptible to external factors, such as the number of people present in a room, the shape and dimension of an environment, and the positions of objects and devices. To improve the localization accuracy of trilateration, we develop a novel distributed localization algorithm with a dynamic-circle-expanding mechanism capable of more accurately establishing the geometric relationship between an unknown node and reference nodes. The results of real world experiments and computer simulation show that the average error of position estimation is 0.67 and 0.225 m in the best cases, respectively. This suggests that the proposed localization algorithm outperforms other existing methods.

Ant colony optimization for best path planning

The paper presents an optimal approach to search the best path of a map considering the traffic loading conditions. The main objective of this work is to minimize the path length to get the best path planning for a given map. This study proposes a solution algorithm based on the ant colony optimization technique to search the shortest path from a desired origin to a desired destination of the map. The proposed algorithm is implemented in C++. Furthermore, the simulation program can randomly generate maps for evaluating its flexibility and performance. Simulation results demonstrate that the proposed algorithm can obtain the shortest path of a map with fast speed.

A contour based image segmentation algorithm using morphological edge detection

In this paper, a novel approach for edge-based image segmentation is proposed. Image segmentation and object extraction play an important role in supporting content-based image coding, indexing, and retrieval. However, its always a tough task to partition an object in a graph-based image. We proposed an image segmentation algorithm by integrating mathematical morphological edge detector with region growing technique. The images are first enhanced by morphological closing operations, and then detect the edge of the image by morphological dilation residue edge detector. Moreover, we deploy growing seeds into the edge image that obtained by the edge detection procedure. By cross comparing the growing result and the detected edges, the partition lines of the image are generated. In this paper, we presented the theoretical backgrounds and procedure illustrations of the proposed algorithm. Furthermore, the proposed algorithm is implemented in C++ language and evaluate on several images with promising results.

A pattern recognition approach to infer time-lagged genetic interactions

MOTIVATION For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags. In each subclass, PARE extracts non-linear characteristics of paired gene-expression curves and learns weights of the decision score applying an optimization algorithm to microarray gene-expression data (MGED) of some known interactions, from biological experiments or published literature. Namely, PARE integrates both MGED and existing knowledge via machine learning, and subsequently predicts the other genetic interactions in the subclass. RESULTS PARE, a time-lagged correlation approach and the latest advance in graphical Gaussian models were applied to predict 112 (132) pairs of TC/TD (transcriptional regulatory) interactions. Checked against qRT-PCR results (published literature), their true positive rates are 73% (77%), 46% (51%), and 52% (59%), respectively. The false positive rates of predicting TC and TD (AT and RT) interactions in the yeast genome are bounded by 13 and 10% (10 and 14%), respectively. Several predicted TC/TD interactions are shown to coincide with existing pathways involving Sgs1, Srs2 and Mus81. This reinforces the possibility of applying genetic interactions to predict pathways of protein complexes. Moreover, some experimentally testable gene interactions involving DNA repair are predicted. AVAILABILITY Supplementary data and PARE software are available at http://www.stat.sinica.edu.tw/~gshieh/pare.htm.

Inferring genetic interactions via a nonlinear model and an optimization algorithm

BackgroundBiochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.ResultsAn S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.ConclusionsGASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

A QoS-guaranteed coverage precedence routing algorithm for wireless sensor networks.

For mission-critical applications of wireless sensor networks (WSNs) involving extensive battlefield surveillance, medical healthcare, etc., it is crucial to have low-power, new protocols, methodologies and structures for transferring data and information in a network with full sensing coverage capability for an extended working period. The upmost mission is to ensure that the network is fully functional providing reliable transmission of the sensed data without the risk of data loss. WSNs have been applied to various types of mission-critical applications. Coverage preservation is one of the most essential functions to guarantee quality of service (QoS) in WSNs. However, a tradeoff exists between sensing coverage and network lifetime due to the limited energy supplies of sensor nodes. In this study, we propose a routing protocol to accommodate both energy-balance and coverage-preservation for sensor nodes in WSNs. The energy consumption for radio transmissions and the residual energy over the network are taken into account when the proposed protocol determines an energy-efficient route for a packet. The simulation results demonstrate that the proposed protocol is able to increase the duration of the on-duty network and provide up to 98.3% and 85.7% of extra service time with 100% sensing coverage ratio comparing with LEACH and the LEACH-Coverage-U protocols, respectively.

A novel optimization algorithm: space gravitational optimization

A new concept for the optimization of nonlinear functions is proposed. For most of the proposed evolutionary optimization algorithms, such as particle swarm optimization and ant colony optimization, they search the solution space by sharing known knowledge. The proposed algorithm is based on the Einsteins general theory of relativity, which we utilize the concept of gravitational field to search for the global optimal solution for a given problem. In this paper, detail procedure of the proposed algorithm is introduced. The proposed algorithm has been tested on an application that is known difficult with promising and exciting results.

Robust multiple objects tracking using image segmentation and trajectory estimation scheme in video frames

Abstract In this paper, a novel image segmentation and a robust unsupervised video objects tracking algorithm are proposed. The proposed method is able to track complete object regions in a sequence of video frames. In this work, object tracking is achieved by analysing the movement of the contours with frame by frame in the video stream. The proposed algorithm involves with three major components for analysing the shapes and motions of the object in the video frames. First, a modified mathematical morphology edge detection algorithm is utilized to extract the contour features in the video frames. Then, a contour-based image segmentation algorithm is proposed and applied to the contour features for partitioning the predetermined target objects in the video frames. Finally, a trajectory estimation scheme is developed to handle the movements of the objects in the video frames. The proposed image segmentation algorithm is capable of automatically partitioning the predetermined objects. The proposed tracking algorithm is also robust against overlapping and videos acquired by non-stationary cameras. The experimental results show that the proposed algorithm can precisely partition and track the predetermined objects in video frames.

High-Precision RSSI-based Indoor Localization Using a Transmission Power Adjustment Strategy for Wireless Sensor Networks

Indoor localization is an important issue in wireless sensor network (WSN) studies. Sensed data may become meaningless, if the locations of sensors are not known. Traditional localization techniques do not meet the requirements of low-cost and energy-conservation while performing localization tasks. Recently, the received signal strength indicator (RSSI)-based range measurement technology is widely used in sensor networks due to its easy implementation. In typical indoor environments, RSSI is affected by dense multipath fading effects because people are moving around, or because furniture and equipment block transmission signals. Therefore, the overall accuracy of RSSI-based localization schemes remains low. In this paper, a new localization scheme which is based on a transmission power adjustment strategy is proposed. Firstly, power decay curves are created in a real indoor environment to accurately estimate the distances between an unknown node and anchor nodes. Secondly, the unknown node selects three nearest anchor nodes by using the minimum transmission power to limit the estimated location to a triangle area. And then, the centroid of the triangle is calculated and serves as the initial estimated point. Finally, based on the estimated distances of corresponding power curves determined by RSSI scores using different transmission power levels, the final estimated location falls in one of the three equally divided areas of the triangle. The experimental results demonstrate that the proposed method can provide a low-cost solution for indoor localization with high precision.

CoCMA: Energy-Efficient Coverage Control in Cluster-Based Wireless Sensor Networks Using a Memetic Algorithm

Deployment of wireless sensor networks (WSNs) has drawn much attention in recent years. Given the limited energy for sensor nodes, it is critical to implement WSNs with energy efficiency designs. Sensing coverage in networks, on the other hand, may degrade gradually over time after WSNs are activated. For mission-critical applications, therefore, energy-efficient coverage control should be taken into consideration to support the quality of service (QoS) of WSNs. Usually, coverage-controlling strategies present some challenging problems: (1) resolving the conflicts while determining which nodes should be turned off to conserve energy; (2) designing an optimal wake-up scheme that avoids awakening more nodes than necessary. In this paper, we implement an energy-efficient coverage control in cluster-based WSNs using a Memetic Algorithm (MA)-based approach, entitled CoCMA, to resolve the challenging problems. The CoCMA contains two optimization strategies: a MA-based schedule for sensor nodes and a wake-up scheme, which are responsible to prolong the network lifetime while maintaining coverage preservation. The MA-based schedule is applied to a given WSN to avoid unnecessary energy consumption caused by the redundant nodes. During the network operation, the wake-up scheme awakens sleeping sensor nodes to recover coverage hole caused by dead nodes. The performance evaluation of the proposed CoCMA was conducted on a cluster-based WSN (CWSN) under either a random or a uniform deployment of sensor nodes. Simulation results show that the performance yielded by the combination of MA and wake-up scheme is better than that in some existing approaches. Furthermore, CoCMA is able to activate fewer sensor nodes to monitor the required sensing area.