• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1412-1430
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• 2024

The soft cancellation list (SCANL) decoding algorithm for polar codes runs L soft cancellation (SCAN) decoders with different decoding factor graphs. Although it can achieve better decoding performance than SCAN algorithm, it has high latency. In this paper, a fast simplified SCANL (Fast-SSCANL) algorithm that runs L independent Fast-SSCAN decoders is proposed. In Fast-SSCANL decoder, special nodes in each factor graph is identified, and corresponding low-latency decoding approaches for each special node is propose first. Then, syndrome check aided Fast-SSCANL (SC-Fast-SSCANL) algorithm is further put forward. The ordinary nodes satisfied the syndrome check will execute hard decision directly without traversing the factor graph, thereby reducing the decoding latency further. Simulation results show that Fast-SSCANL and SC-Fast-SSCANL algorithms can achieve the same BER performance as the SCANL algorithm with lower latency. Fast-SSCANL algorithm can reduce latency by more than 83% compared with SCANL, and SC-Fast-SSCANL algorithm can reduce more than 85% latency compared with SCANL regardless of code length and code rate.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.1-6
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• 2009

Localization of nodes is a key technology for application of wireless sensor network. Having a GPS receiver on every sensor node is costly. In the past, several approaches, including range-based and range-free, have been proposed to calculate positions for randomly deployed sensor nodes. Most of them use some special nodes, called anchor nodes, which are assumed to know their own locations. Other sensors compute their locations based on the information provided by these anchor nodes. This paper uses a single mobile anchor node to move in the sensing field and broadcast its current position periodically. We provide a weighted centroid localization algorithm that uses coefficients, which are decided by the influence of mobile anchor node to unknown nodes, to prompt localization accuracy. We also suggest a criterion which is used to select mobile anchor node which involve in computing the position of nodes for improving localization accuracy. Weighted centroid localization algorithm is simple, and no communication is needed while locating. The localization accuracy of weighted centroid localization algorithm is better than maximum likelihood estimation which is used very often. It can be applied to many applications.

• Journal of IKEEE
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• v.11 no.4
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• pp.297-306
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• 2007

A sensor network is composed of a large number of sensor nodes that are densely deployed in a field. Each sensor performs a sensing task for detection specific events. After detecting this event, location information of the sensor node is very important. Range-based scheme of the proposed approaches typically achieve high accuracy on either node-to-node distances or angles, but this scheme have a drawback because all sensor nodes have the special hardware. On the other hand, range-free scheme provides economic advantage because of no needed hardware even if that leads to coarse positioning accuracy. In this paper, we propose a range-free localization algorithm without range information in wireless sensor networks. This is a range-free approach and uses a small number of anchor nodes and known sensor nodes. This paper develops a localization mechanism using the geometry conjecture (perpendicular bisector of a chord). The conjecture states that a perpendicular bisector of a chord passes through the center of the circle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1571-1589
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• 2016

Nodes in MANETs are battery powered which makes energy an invaluable resource. In OLSR, MPRs are special nodes that are selected by other nodes to relay their data/control traffic which may lead to high energy consumption of MPR nodes. Therefore, employing energy efficient MPR selection mechanism is imperative to ensure prolonged network lifetime. However, misbehaving MPR nodes tend to preserve their energy by dropping packets of other nodes instead of forwarding them. This leads to huge energy loss and performance degradation of existing energy efficient MPR selection schemes. This paper proposes an energy efficient secure MPR selection (ES-MPR) technique that takes into account both energy and security metrics for MPR selection. It introduces the concept of 'Composite Eligibility Index' (CEI) to examine the eligibility of a node for being selected as an MPR. CEI is used in conjunction with willingness to provide distinct selection parameters for Flooding and Routing MPRs. Simulation studies reveal the efficiency of ES-MPR in selection of energy efficient secure and stable MPRs, in turn, prolonging the network operational lifetime.

• Journal of the military operations research society of Korea
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• v.17 no.2
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• pp.113-122
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• 1991

This paper presents the Allocation Priority Scheme (APS) for multiprocessor system. The objective of APS is to reduce the time-complexity on a Physical Mapping Scheme(PMS). The PMS is to allocate the nodes of the Data Dependency Graph (DDG) to the multprocessors efficiently and effectively. The APS provides the priority to each node (vertex) in the DDG. In other words, the goal of the APS is to find a request resource mapping such that the total cost (time-complexity) is minimized. The special case in which all requests have equal priorities and all resoruces have equal precedences, and the comparisons between our APS and other schems are discussed in the paper. The APS provides the heuristic rules which are based on maximum height (MH), number of children nodes ($N_c$), number of father nodes ($N_f$), and computation time ($T_c$). The estimation moth of the computaion time is in the paper.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.2
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• pp.183-189
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• 2014

Pseudo-spectral method typically converges at an exponential rate. However, it requires a special set of fixed collocation points (CPs) to get highly accurate formulas for partial integration and differentiation. In this study, computational nodes for defining the discrete variables of states and controls are built independently of the CPs. The state and control variables at each CP, which are required to transcribe an NOCP into the corresponding NLP, are interpolated, using those variables allocated at each node. Additionally, Lagrange interpolation and spline interpolation are investigated, to provide a guideline for selecting a favorable interpolation method. The proposed techniques are applied to the solution of an NOCP using equally spaced nodes, and the computed results are compared to those using the standard PS approach, to validate the usefulness of the proposed methods.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.28.3-28
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• 2001

This paper proposes a new algorithm that detects and separates the occluding and occluded objects in a 2D image. An input image is represented by the attributed graph where a node corresponds to a surface and an arc connecting two nodes describes the adjacency of the nodes in the image. Each end of arc is weighted by relation value which tells the number of edges connected to the surface represented by the node in the opposite side of the arc. In attributed graph homogeneous nodes pertained to the same object always construct one of three special patterns which can be simply classified by comparison of relation values of the arcs. The experimental results have shown that the proposed algorithm efficiently separates the objects ...

• Journal of Information Processing Systems
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• v.5 no.4
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• pp.167-174
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• 2009

Nowadays, it has become common to equip a device with Bluetooth. As such devices become pervasive in the world; much work has been done on forming them into a network, however, almost all the Bluetooth Scatternet Formation Algorithms assume devices are homogeneous. Even the exceptional algorithms barely mentioned a little about the different characteristics of devices like computational abilities, traffic loads for special nodes like bridge nodes or super nodes, which are usually the bottleneck in the scatternet. In this paper, we treat the devices differently not only based on the hardware characteristics, but also considering other conditions like different classes, different groups and so on. We use a two-phase Scatternet Formation Algorithm here: in the first phase, construct scatternets for a specified kind of devices; in the second phase, connect these scatternets by using least other kinds of devices as bridge nodes. Finally, we give some applications to show the benefit of classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4971-4987
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• 2019

Node localization is the basic task of underwater wireless sensor networks (UWSNs). Most of the existing underwater localization methods rely on ranging accuracy. Due to the special environment conditions in the ocean, beacon nodes are difficult to deploy accurately. The narrow bandwidth and high delay of the underwater acoustic communication channel lead to large errors. In order to reduce the ranging error and improve the positioning accuracy, we propose a localization algorithm based on ranging correction and inertial coordination. The algorithm can be divided into two parts, Range Correction based Localization algorithm (RCL) and Inertial Coordination based Localization algorithm (ICL). RCL uses the geometric relationship between the node positions to correct the ranging error and obtain the exact node position. However, when the unknown node deviates from the deployment area with the movement of the water flow, it cannot communicate with enough beacon nodes in a certain period of time. In this case, the node uses ICL algorithm to combine position data with motion information of neighbor nodes to update its position. The simulation results show that the proposed algorithm greatly improves the positioning accuracy of unknown nodes compared with the existing localization methods.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.79-89
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• 1998

The Error Back-Propagation(EBP) algorithm is widely applied to train a multi-layer perceptron, which is a neural network model frequently used to solve complex problems such as pattern recognition, adaptive control, and global optimization. However, the EBP is basically a gradient descent method, which may get stuck in a local minimum, leading to failure in finding the globally optimal solution. Moreover, a multi-layer perceptron suffers from locking a systematic determination of the network structure appropriate for a given problem. It is usually the case to determine the number of hidden nodes by trial and error. In this paper, we propose a new algorithm to efficiently train a multi-layer perceptron. OUr algorithm uses stochastic perturbation in the weight space to effectively escape from local minima in multi-layer perceptron learning. Stochastic perturbation probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the EGP learning gets stuck to it. Addition of new hidden nodes also can be viewed asa special case of stochastic perturbation. Using stochastic perturbation we can solve the local minima problem and the network structure design in a unified way. The results of our experiments with several benchmark test problems including theparity problem, the two-spirals problem, andthe credit-screening data show that our algorithm is very efficient.