The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation (TFSA). The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.

With the rapid development of the Internet, recent years have seen the explosive growth of social media. This brings great challenges in performing efficient and accurate image retrieval on a large scale. Recent work shows that using hashing methods to embed high-dimensional image features and tag information into Hamming space provides a powerful way to index large collections of social images. By learning hash codes through a spectral graph partitioning algorithm, spectral hashing (SH) has shown promising performance among various hashing approaches. However, it is incomplete to model the relations among images only by pairwise simple graphs which ignore the relationship in a higher order. In this paper, we utilize a probabilistic hypergraph model to learn hash codes for social image retrieval. A probabilistic hypergraph model offers a higher order representation among social images by connecting more than two images in one hyperedge. Unlike a normal hypergraph model, a probabilistic hypergraph model considers not only the grouping information, but also the similarities between vertices in hyperedges. Experiments on Flickr image datasets verify the performance of our proposed approach.

In this paper, spatial channel pairing (SCP) is introduced to coherent combining at the relay in relay networks. Closed-form solution to optimal coherent combining is derived. Given coherent combining, the approximate SCP solution is presented. Finally, an alternating iterative structure is developed. Simulation results and analysis show that, given the symbol error rate and data rate, the proposed alternating iterative structure achieves signal-to-noise ratio gains over existing schemes in maximum ratio combining (MRC) plus matched filter, MRC plus antenna selection, and distributed space-time block coding due to the use of SCP and iterative structure.

Ball grid arrays (BGAs) have been used in the production of electronic devices/assemblies because of their advantages of small size, high I/O port density, etc. However, BGA voids can degrade the performance of the board and cause failure. In this paper, a novel blob filter is proposed to automatically detect BGA voids presented in X-ray images. The proposed blob filter uses the local image gradient magnitude and thus is not influenced by image brightness, void position, or component interference. Different sized average box filters are employed to analyze the image in multi-scale, and as a result, the proposed blob filter is robust to void size. Experimental results show that the proposed method obtains void detection accuracy of up to 93.47% while maintaining a low false ratio. It outperforms another recent algorithm based on edge detection by 40.69% with respect to the average detection accuracy, and by 16.91% with respect to the average false ratio.

Contemporary operating systems for single-ISA (instruction set architecture) multi-core systems attempt to distribute tasks equally among all the CPUs. This approach works relatively well when there is no difference in CPU capability. However, there are cases in which CPU capability differs from one another. For instance, static capability asymmetry results from the advent of new asymmetric hardware, and dynamic capability asymmetry comes from the operating system (OS) outside noise caused from networking or I/O handling. These asymmetries can make it hard for the OS scheduler to evenly distribute the tasks, resulting in less efficient load balancing. In this paper, we propose a user-level load balancer for parallel applications, called the ‘capability balancer’, which recognizes the difference of CPU capability and makes subtasks share the entire CPU capability fairly. The balancer can coexist with the existing kernel-level load balancer without detrimenting the behavior of the kernel balancer. The capability balancer can fairly distribute CPU capability to tasks with very little overhead. For real workloads like the NAS Parallel Benchmark (NPB), we have accomplished speedups of up to 9.8% and 8.5% in dynamic and static asymmetries, respectively. We have also experienced speedups of 13.3% for dynamic asymmetry and 24.1% for static asymmetry in a competitive environment. The impacts of our task selection policies, FIFO (first in, first out) and cache, were compared. The use of the cache policy led to a speedup of 5.3% in overall execution time and a decrease of 4.7% in the overall cache miss count, compared with the FIFO policy, which is used by default.

The ranging accuracy of a pseudo-noise ranging system is mainly decided by range jitter and time delay discrimination. Many factors can affect the ranging accuracy, one of which is the chip rate. In digital signal processing, the time delay discrimination and autocorrelation function of sampled ranging sequences of different chip rates are very different. An approximation simulation model is established according to an in-phase quadrature (I/Q) correlator which is used to evaluate the time delay. Simulation results of the range jitter and time delay discrimination show that the chip rate which provides a non-integer sample-to-chip rate ratio can achieve a higher ranging accuracy, and some test results validate the simulation model. In some design missions, the simulation results may help to select an optimum sample-to-chip rate ratio to satisfy the design requirement on the ranging accuracy.

We develop a new kind of underwater inductive coupling power transfer (ICPT) system to evaluate wireless power transfer in autonomous underwater vehicle (AUV) docking applications. Parameters that determine the performance of the system are systematically analyzed through mathematical methods. A circuit simulation model and a finite element analysis (FEA) simulation model are developed to study the power losses of the system, including copper loss in coils, semiconductor loss in circuits, and eddy current loss in transmission media. The characteristics of the power losses can provide guidelines to improve the efficiency of ICPT systems. Calculation results and simulation results are validated by relevant experiments of the prototype system. The output power of the prototype system is up to 45 W and the efficiency is up to 0.84. The preliminary results indicate that the efficiency will increase as the transmission power is raised by increasing the input voltage. When the output power reaches 500 W, the efficiency is expected to exceed 0.94. The efficiency can be further improved by choosing proper semiconductors and coils. The analysis methods prove effective in predicting the performance of similar ICPT systems and should be useful in designing new systems.

To obtain comparable high query performance with relational databases, diverse database technologies have to be adapted to confront the complexity posed by both Resource Description Framework (RDF) data and SPARQL query. Database caching is one of such technologies that improves the performance of database with reasonable space expense based on the spatial/ temporal/semantic locality principle. However, existing caching schemes exploited in RDF stores are found to be dysfunctional for complex query semantics. Although semantic caching approaches work effectively in this case, little work has been done in this area. In this paper, we try to improve SPARQL query performance with semantic caching approaches, i.e., SPARQL algebraic expression tree (AET) based caching and entity caching. Successive queries with multiple identical sub-queries and star-shaped joins can be efficiently evaluated with these two approaches. The approaches are implemented on a two-level-storage structure. The main memory stores the most frequently accessed cache items, and items swapped out are stored on the disk for future possible reuse. Evaluation results on three mainstream RDF benchmarks illustrate the effectiveness and efficiency of our approaches. Comparisons with previous research are also provided.

To provide pest technicians with a convenient way to recognize insects, a novel method is proposed to classify insect images by integrated region matching (IRM) and dual tree complex wavelet transform (DTCWT). The wing image of the lepidopteran insect is preprocessed to obtain the region of interest (ROI) whose position is then calibrated. The ROI is first segmented with the k-means algorithm into regions according to the color features, properties of all the segmented regions being used as a coarse level feature. The color image is then converted to a grayscale image, where DTCWT features are extracted as a fine level feature. The IRM scheme is undertaken to find K nearest neighbors (KNNs), out of which the nearest neighbor is searched by computing the Canberra distance of DTCWT features. The method was tested with a database including 100 lepidopteran insect species from 18 families and the recognition accuracy was 84.47%. For the forewing subset, a recognition accuracy of 92.38% was achieved. The results showed that the proposed method can effectively solve the problem of automatic species identification of lepidopteran specimens.

We present a novel method to implement the radix-2 fast Fourier transform (FFT) algorithm on field programmable gate arrays (FPGA). The FFT architecture exploits parallelism by having more pipelined units in the stages, and more parallel units within a stage. It has the noticeable advantages of high speed and more efficient resource utilization by employing four ganged butterfly engines (GBEs), and can be well matched to the placement of the resources on the FPGA. We adopt the decimation-in-frequency (DIF) radix-2 FFT algorithm and implement the FFT processor on a state-of-the-art FPGA. Experimental results show that the processor can compute 1024-point complex radix-2 FFT in about 11 μs with a clock frequency of 200 MHz.

A conference key establishment protocol allows a group of conferees to agree on a secret key shared among them for secure group communication. This paper proposes a three-level conference key establishment protocol based on the Universal Mobile Telecommunications System (UMTS) framework to establish a group-level key, home location register (HLR) level keys, and visitor location register (VLR) level keys simultaneously for a group of conferees. The group-level key is used to secure the communications for all conferees, the HLR-level key is for those within the same HLR domain, and the VLR-level key is for those within the same VLR domain. The group-level key can be used for securing inter-domain group-oriented applications such as commercial remote conferencing systems. The HLR- and VLR-level keys can be used for securing intra-domain subgroup applications (e.g., location-based or context-aware services) and dynamic key updating. Since our proposed protocol exploits existing UMTS security functions and the exclusive-or operation, it is compatible with UMTS architecture. This means that it is fast and easy to implement on the existing UMTS architecture. Furthermore, the proposed protocol has low computational complexities and can provide cost effectiveness, load-amortization, scalability, user authentication, key establishment, key confirmation, key updating, and lawful interception.

The forwarding address plays an important role in constructing a communication network. In this paper, a new forwarding address suitable for next generation networks named the vector address (VA) is proposed which is different from the forwarding address coding methods of current networks. The characteristics of the VA are analyzed. Complex network theory and a theoretical analysis method are introduced to study the average address length of the VA when used to construct a global network. Simulation experiments in a practical network topology model are carried out to validate the results. The results show that not only can the VA construct a simpler, more secure, and more scalable network, but it also can accommodate many more users than an Internet Protocol (IP) network with the same address length.

Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS (branch-and-bound skyline with rescanning and repacking) and TPBBS (time-parameterized branch-and-bound skyline), each with two alternative methods, to handle predictive skyline computation. The basic TPBBS is further extended to TPBBSE (TPBBS with expansion) to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.

We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single delay in the state vector. We first derive an equivalent linear time-invariant (LTI) system for the time-delay system using a state augmentation technique. Then a conventional subspace identification method is used to estimate augmented system matrices and Kalman state sequences up to a similarity transformation. To obtain a state-space model for the time-delay system, an alternate convex search (ACS) algorithm is presented to find a similarity transformation that takes the identified augmented system back to a form so that the time-delay system can be recovered. Finally, we reconstruct the Kalman state sequences based on the similarity transformation. The time-delay system matrices under the same state-space basis can be recovered from the Kalman state sequences and input-output data by solving two least squares problems. Numerical examples are to show the effectiveness of the proposed method.

A virtual power plant (VPP) can realize the aggregation of distributed generation in a certain region, and represent distributed generation to participate in the power market of the main grid. With the expansion of VPPs and ever-growing heat demand of consumers, managing the effect of fluctuations in the amount of available renewable resources on the operation of VPPs and maintaining an economical supply of electric power and heat energy to users have been important issues. This paper proposes the allocation of an electric boiler to realize wind power directly converted for supplying heat, which can not only overcome the limitation of heat output from a combined heat and power (CHP) unit, but also reduce carbon emissions from a VPP. After the electric boiler is considered in the VPP operation model of the combined heat and power system, a multi-objective model is built, which includes the costs of carbon emissions, total operation of the VPP and the electricity traded between the VPP and the main grid. The model is solved by the CPLEX package using the fuzzy membership function in Matlab, and a case study is presented. The power output of each unit in the case study is analyzed under four scenarios. The results show that after carbon emission is taken into account, the output of low carbon units is significantly increased, and the allocation of an electric boiler can facilitate the maximum absorption of renewable energy, which also reduces carbon emissions from the VPP.

Despite the critical role that middleboxes play in introducing new network functionality, management and innovation of them are still severe challenges for network operators, since traditional middleboxes based on hardware lack service flexibility and scalability. Recently, though new networking technologies, such as network function virtualization (NFV) and software-defined networking (SDN), are considered as very promising drivers to design cost-efficient middlebox service architectures, how to guarantee transmission efficiency has drawn little attention under the condition of adding virtual service process for traffic. Therefore, we focus on the service deployment problem to reduce the transport delay in the network with a combination of NFV and SDN. First, a framework is designed for service placement decision, and an integer linear programming model is proposed to resolve the service placement and minimize the network transport delay. Then a heuristic solution is designed based on the improved quantum genetic algorithm. Experimental results show that our proposed method can calculate automatically the optimal placement schemes. Our scheme can achieve lower overall transport delay for a network compared with other schemes and reduce 30% of the average traffic transport delay compared with the random placement scheme.

The operational environment of today’s smart grids is becoming more complicated than ever before. A number of factors, including renewable penetration, marketization, cyber security, and hazards of nature, bring challenges and even threats to control centers. New techniques are anticipated to help dispatchers become aware of the accurate situations as they manipulate and navigate the situations as quickly as possible. To address the issues, we first introduce the background for this topic as well as the emerging technical demands of situational awareness in the dispatcher’s environment. The general concepts and technical requirements of situational awareness are then summarized, aimed at offering an overview for readers to understand the state-of-the-art progress in this area. In addition, we discuss the importance of integrating the architecture of support tools in accordance with the dispatcher’s thought process, which in fact guides correct and swift reactions in real-time operations. Finally, the prospects for situational awareness architecture are investigated with the goal of presenting situational awareness modules in an advanced and visualized manner.

The floating point number is the most commonly used real number representation for digital computations due to its high precision characteristics. It is used on computers and on single chip applications such as DSP chips. Double precision (64-bit) representations allow for a wider range of real numbers to be denoted. However, single precision (32-bit) operations are more efficient. Recently, there has been an increasing interest in mixed precision computations which take advantage of single precision efficiency on 64-bit numbers. This calls for the ability to interchange between the two formats. In this paper, an algorithm that converts floating point numbers from 64- to 32-bit representations is presented. The algorithm was implemented as a Verilog code and tested on field programmable gate array (FPGA) using the Quartus II DE2 board and Agilent 16821A portable logic analyzer. Results indicate that the algorithm can perform the conversion reliably and accurately within a constant execution time of 25 ns with a 20 MHz clock frequency regardless of the number being converted.

The problem of finding stabilizing controllers for switched systems is an area of much research interest as conventional concepts from continuous time and discrete event dynamics do not hold true for these systems. Many solutions have been proposed, most of which are based on finding the existence of a common Lyapunov function (CLF) or a multiple Lyapunov function (MLF) where the key is to formulate the problem into a set of linear matrix inequalities (LMIs). An alternative method for finding the existence of a CLF by solving two sets of linear inequalities (LIs) has previously been presented. This method is seen to be less computationally taxing compared to methods based on solving LMIs. To substantiate this, the computational ability of three numerical computational solvers, LMI solver, cvx, and Yalmip, as well as the symbolic computational program Maple were tested. A specific switched system comprising four second-order subsystems was used as a test case. From the obtained solutions, the validity of the controllers and the corresponding CLF was verified. It was found that all tested solvers were able to correctly solve the LIs. The issue of rounding-off error in numerical computation based software is discussed in detail. The test revealed that the guarantee of stability became uncertain when the rounding off was at a different decimal precision. The use of different external solvers led to the same conclusion in terms of the stability of switched systems. As a result, a shift from using a conventional numerical computation based program to using computer algebra is suggested.

The K-means method is a well-known clustering algorithm with an extensive range of applications, such as biological classification, disease analysis, data mining, and image compression. However, the plain K-means method is not fast when the number of clusters or the number of data points becomes large. A modified K-means algorithm was presented by Fahim et al. (2006). The modified algorithm produced clusters whose mean square error was very similar to that of the plain K-means, but the execution time was shorter. In this study, we try to further increase its speed. There are two rules in our method: a selection rule, used to acquire a good candidate as the initial center to be checked, and an erasure rule, used to delete one or many unqualified centers each time a specified condition is satisfied. Our clustering results are identical to those of Fahim et al. (2006). However, our method further cuts computation time when the number of clusters increases. The mathematical reasoning used in our design is included.

This paper is devoted to developing and evaluating a set of technologies with the objective of designing a methodology for the implementation of sophisticated traffic lights by means of rational agents. These devices would be capable of optimizing the behavior of a junction with multiple traffic signals, reaching a higher level of autonomy without losing reliability, accuracy, or efficiency in the offered services. In particular, each rational agent in a traffic signal will be able to analyze the requirements and constraints of the road, in order to know its level of demand. With such information, the rational agent will adapt its light cycles with the view of accomplishing more fluid traffic patterns and minimizing the pollutant environmental emissions produced by vehicles while they are stopped at a red light, through using a case-based reasoning (CBR) adaptation. This paper also integrates a microscopic simulator developed to run a set of tests in order to compare the presented methodology with traditional traffic control methods. Two study cases are shown to demonstrate the efficiency of the introduced approach, increasing vehicular mobility and reducing harmful activity for the environment. For instance, in the first scenario, taking into account the studied traffic volumes, our approach increases mobility by 23% and reduces emissions by 35%. When the roads are managed by sophisticated traffic lights, a better level of service and considerable environmental benefits are achieved, demonstrating the utility of the presented approach.

Rise in inlet air temperature increases the corresponding outlet air temperature from the server. As an added effect of rise in inlet air temperature, some active servers may start exhaling intensely hot air to form a hotspot. Increase in hot air temperature and occasional hotspots are an added burden on the cooling mechanism and result in energy wastage in data centers. The increase in inlet air temperature may also result in failure of server hardware. Identifying and comparing the thermal sensitivity to inlet air temperature for various servers helps in the thermal-aware arrangement and location switching of servers to minimize the cooling energy wastage. The peak outlet temperature among the relocated servers can be lowered and even be homogenized to reduce the cooling load and chances of hotspots. Based upon mutual comparison of inlet temperature sensitivity of heterogeneous servers, this paper presents a proactive approach for thermal-aware relocation of data center servers. The experimental results show that each relocation operation has a cooling energy saving of as much as 2.1 kW·h and lowers the chances of hotspots by over 77%. Thus, the thermal-aware relocation of servers helps in the establishment of green data centers.

We propose an effective optimization method for generating smooth freeform surfaces for light-emitting diode (LED) non-rotational illumination based on ray targeting. This method begins with a starting design and goes through two optimization steps. An initial estimate is determined using a partial differential equation (PDE) method and a variable separation mapping. In the first optimization step the merit function is developed with ray targeting to ensure the shape of the illumination pattern. The purpose of the second optimization is to further improve the optical performance by constructing the merit function with uniformity and efficiency. Smooth freeform reflective and refractive surfaces, which can produce a uniform rectangular illumination without rotational symmetry, are designed using this method. The results show that uniform rectangular illumination is achieved and that smooth freeform surfaces are obtained. With ray targeting, the design efficiency can be significantly enhanced, and excellent optical performance can be achieved.

Vector graphic , as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is {image meshing}, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.

We introduce a kind of shape-adjustable spline curves defined over a non-uniform knot sequence. These curves not only have the many valued properties of the usual non-uniform B-spline curves, but also are shape adjustable under fixed control polygons. Our method is based on the degree elevation of B-spline curves, where maximum degrees of freedom are added to a curve parameterized in terms of a non-uniform B-spline. We also discuss the geometric effect of the adjustment of shape parameters and propose practical shape modification algorithms, which are indispensable from the user’s perspective.

This paper is concerned with the routing protocol design for large-scale wireless sensor and actor networks (WSANs). The actor-sensor-actor communication (ASAC) strategy is first proposed to guarantee the reliability of persistent actor-actor communication. To keep network connectivity and prolong network lifetime, we propose a dynamic gradient-based routing protocol (DGR) to balance the energy consumption of the network. With the different communication ranges of sensors and actors, the DGR protocol uses a data load expansion strategy to significantly prolong the network lifetime. The balance coefficient and the routing re-establishment threshold are also introduced to make the tradeoff between network lifetime and routing efficiency. Simulation results show the effectiveness of the proposed DGR protocol for unbalanced and persistent data transmission.

One recent area of interest in computer science is data stream management and processing. By ‘data stream’, we refer to continuous and rapidly generated packages of data. Specific features of data streams are immense volume, high production rate, limited data processing time, and data concept drift; these features differentiate the data stream from standard types of data. An issue for the data stream is classification of input data. A novel ensemble classifier is proposed in this paper. The classifier uses base classifiers of two weighting functions under different data input conditions. In addition, a new method is used to determine drift, which emphasizes the precision of the algorithm. Another characteristic of the proposed method is removal of different numbers of the base classifiers based on their quality. Implementation of a weighting mechanism to the base classifiers at the decision-making stage is another advantage of the algorithm. This facilitates adaptability when drifts take place, which leads to classifiers with higher efficiency. Furthermore, the proposed method is tested on a set of standard data and the results confirm higher accuracy compared to available ensemble classifiers and single classifiers. In addition, in some cases the proposed classifier is faster and needs less storage space.

The data center network (DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.

With the deployment of heterogeneous networks, mobile users are expecting ubiquitous connectivity when using applications. For bandwidth-intensive applications such as Internet Protocol Television (IPTV), multimedia contents are typically transmitted using a multicast delivery method due to its bandwidth efficiency. However, not all networks support multicasting. Multicasting alone could lead to service disruption when the users move from a multicast-capable network to a non-multicast network. In this paper, we propose a handover scheme called application layer seamless switching (ALSS) to provide smooth real-time multimedia delivery across unicast and multicast networks. ALSS adopts a soft handover to achieve seamless playback during the handover period. A real-time streaming testbed is implemented to investigate the overall handover performance, especially the overlapping period where both network interfaces are receiving audio and video packets. Both the quality of service (QoS) and objective-mapped quality of experience (QoE) metrics are measured. Experimental results show that the overlapping period takes a minimum of 56 and 4 ms for multicast-to-unicast (M2U) and unicast-to-multicast (U2M) handover, respectively. The measured peak signal-to-noise ratio (PSNR) confirms that the frame-by-frame quality of the streamed video during the handover is at least 33 dB, which is categorized as good based on ITU-T recommendations. The estimated mean opinion score (MOS) in terms of video playback smoothness is also at a satisfactory level.

A low-cost fiber Bragg grating (FBG) sensing system for coal-mine security monitoring is proposed in this paper. Based on the coherence multiplexing (CM) and spatial division multiplexing (SDM) techniques, this hybrid sensing network can support more than 40 sensors for quasi-distributed detection. It is demonstrated experimentally that the multiplexed sensing signal of each sensor can be clearly distinguished by an optical low-coherence reflectometry (OLCR). Methane concentration is detected with maximum sensitivities of an intensity variation of 10.92% and a concentration variation of 1%, using a well-designed sensor structure. Strain and temperature are also detected by this system, which also exhibits good results in the experiment.