Earticle

This paper will focus on improving the performance of orthogonal frequency division multiplexing (OFDM) in Rayleigh fading environments. The proposed technique will use a previously published method that has been shown to improve OFDM performance in independent fading, based on ordered sub-carrier selection. Then, a simple non-iterative method for finding the optimal bit-loading allocation was proposed. It was also based on ordered sub-carrier selection. We compared both of these algorithms to an optimal bit-loading solution to determine their effectiveness in a correlated fading environment. The correlated fading was simulated using the JTC channel models. Our intent was not to create an optimal solution, but to create a low complexity solution that can be used in a wireless environment in which the channel conditions change rapidly and that require a simple algorithm for fast bit loading.

Background: This study aims to calculate detector positions as a design of a radioactive source localizing radiation portal monitor (RPM) system using an improved genetic algorithm. Materials and Methods: To calculate of detector positions for a source localizing RPM system optimization problem is defined. To solve the problem, a modified iterative genetic algorithm (MIGA) is developed. In general, a genetic algorithm (GA) finds a globally optimal solution with a high probability, but it is not perfect at all times. To increase the probability to find globally optimal solution rather, a MIGA is designed by supplementing the iteration, competition, and verification with GA. For an optimization problem that is defined to find detector positions that maximizes differences of detector signals, a localization method is derived by modifying the inverse radiation transport model, and realistic parameter information is suggested. Results and Discussion: To compare the MIGA and GA, both algorithms are implemented in a MATLAB environment. The performance of the GA and MIGA and that of the procedures supplemented in the MIGA are analyzed by computer simulations. The results show that the iteration, competition, and verification procedures help to search for globally optimal solutions. Further, the MIGA is more robust against falling into local minima and finds a more reliably optimal result than the GA. Conclusion: The positions of the detectors on an RPM for radioactive source localization are optimized using the MIGA. To increase the contrast of the measurements from each detector, a relationship between the source and the detectors is derived by modifying the inverse transport model. Realistic parameters are utilized for accurate simulations. Furthermore, the MIGA is developed to achieve a reliable solution. By utilizing results of this study, an RPM for radioactive source localization has been designed and will be fabricated soon.

This paper deals with a approximation and tracking control of hydraulic servo system using a real time recurrent neural networks (RTRN) with 2-dimensional iterative learning rule. And it was driven that 2-dimensional iterative learning rule in discrete time. In order to control the trajectory of position, two RTRN with same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm is able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RTRN was very effective to control trajectory tracking of electro-hydraulic servo system.

최근 데이터의 폭발적인 증가로 인해 대규모 데이터의 분석에 대한 요구를 충족할 수 있는 방 법들이 계속 연구되고 있다. 본 논문에서는 맵리듀스를 이용한 분산 병렬 처리를 통해 대규모 데이터 큐브의 효율적인 계산이 가능한 MRIterativeBUC 알고리즘을 제안하였다. MRIterativeBUC 알고 리즘은 기존의 BUC 알고리즘을 맵리듀스의 반복적 단계에 따른 효율적인 동작이 가능하도록 개발되 었고, 기존의 대규모 데이터 큐브 계산에 따른 문제인 데이터 크기와 저장 및 처리 능력의 한계를 해 결하였다. 또한, 분석자의 관심 부분에 대해서만 계산하는 빙산 큐브 개념의 도입과 파티셔닝, 정렬과 같은 큐브 계산을 분산 병렬 처리하는 방법 등의 장점들을 통해 데이터 방출량을 줄여서 네트워크 부 하를 줄이고, 각 노드의 처리량을 줄이며, 궁극적으로 전체 큐브 계산 비용을 줄일 수 있다. 본 연구 결과는 맵리듀스를 이용한 데이터 큐브 계산에 대해서 상향식 처리와 반복적 알고리즘을 통해 다양한 확장이 가능하며, 여러 응용 분야에서 활용이 가능할 것으로 예상된다.

Due to the recent data explosion, methods which can meet the requirement of large data analysis has been studying. This paper proposes MRIterativeBUC algorithm which enables efficient computation of large data cube by distributed parallel processing with MapReduce framework. MRIterativeBUC algorithm is developed for efficient iterative operation of the BUC method with MapReduce, and overcomes the limitations about the storage size and processing ability caused by large data cube computation. It employs the idea from the iceberg cube which computes only the interesting aspect of analysts and the distributed parallel process of cube computation by partitioning and sorting. Thus, it reduces data emission so that it can reduce network overload, processing amount on each node, and eventually the cube computation cost. The bottom-up cube computation and iterative algorithm using MapReduce, proposed in this paper, can be expanded in various way, and will make full use of many applications.

깊이 카메라를 이용해서 인간의 정밀한 전신 포인트 클라우드를 획득하기 위해서는 다양한 위치와 각도에서 촬영한 포인트 클라우드 간의 정합 및 깊이 보정이 필요하다. 기존에는 주로 Iterative Closet Point (ICP) 알고리즘을 이용해서 포인트 클라우드 간의 정합을 위한 3차원 변환을 추정한 후, 후처리로 독립적인 깊이 보정을 수행해 왔다. 본 논문에서는 ICP 알고리즘에서 사용되는 비용함수의 인수로 3차원 변환 뿐만 아니라 포인트 클라우드의 깊이 값이 사용될 수 있다는 점에 착안한 새로운 깊이 보정 알고리즘을 소개하고, 기존 후처리 방법과의 성능 비교를 통해 그 효용성을 검증한다.

무선 통신 시스템에서 재밍 공격은 통신 성능을 심각하게 저하시킬 수 있는 치명적 위협이다. 특히 반응 재밍은 송신기와 수신기가 통신할 때만 재밍할 수 있기 때문에 반응 재밍을 통해 공격 효율을 극대화할 수 있다. 본 논문에서 반응 재밍의 공격 효과를 저감시 키기 위해 multi-input multi-output (MIMO) 기반의 orthogonal frequency-division multiplexing (OFDM) 통신 기술의 특징을 활용 하고, 이에 기반한 반복적 채널 추정 알고리즘을 제안하였다. 실험 결과를 통해 본 논문에서 제안한 알고리즘과 기존 항재밍 알고리 즘을 비교하였으며, 반응 재밍 공격이 있는 MIMO-OFDM 통신 시스템에서 제안한 알고리즘의 우수성을 입증한다.

In wireless communication systems, jamming attack is a critical threat. Especially, reactive jamming can jam when the sender and receiver are communicating, which can maximize the attack efficiency of jamming. In this paper, we use the property of multi-input multi-output (MIMO) technology to achieve jamming resilient orthogonal frequency-division multiplexing (OFDM) communications. In particular, we use MIMO interference cancellation to remove the jamming signals strategically. We first investigate the reactive jamming attack model and their impacts on the MIMO-OFDM systems. We then present an iterative channel estimation algorithm that exploits MIMO interference cancellation. Our simulations show various anti-jamming methods and demonstrate the efficiency of our proposed algorithm under the reactive jamming attack.

통신 시스템에서 제한된 총 전력으로 여러개의 부채널로 이루어진 채널의 입력과 출력 사이의 상호정보를 최 대화하는 문제의 해는 Waterfilling 구조를 가진다. 채널 상태 정보(CSI)를 알고 있을 때 OFDM이나 MIMO는 병렬의 독립된 부채널들로 분해 될 수 있다. 제한된 전력 하에 채널용량에 접근하는 전송속도를 위한 최적의 부채널 전력할당 문제의 해는 Waterfilling 으로 구할 수 있다. Waterfilling은 상태가 좋은(SNR이 높은) 부채 널에 더 많은 전력을 할당 하고 상태가 나쁜 채널들은 적은 전력이나 전력을 할당하지 않음으로서 상태가 좋은 부 채널들의 전송속도를 높이고 결 과적으로 전체 전송속도를 채널용량에 접근하게 한다. Waterfilling은 총 전력 제한을 만 족하는 정확한 수면 높이를 찾 는데 일반적으로 수면 높이를 추정하고 갱신해 나가는 반복적 알고리즘이 사용된다. 이 과정에서 부채널들에 대한 채 널이득 제곱의 역수들의 부분합(             ) 계산이 반복적으로 필요하다. 본 논문에서는 이런 부분합들을 초기화 단계 에서 미리 계산하여 배열을 만들고 임의의 부분합 계산을 배열 참조로 대치함으로서 Waterfilling 알고리즘의 계산 시 간을 줄였다.

In communication systems the solution of the problem of maximizing the mutual information between the input and output of a channel composed of several subchannels under total power constraint has a waterfilling structure. OFDM and MIMO can be decomposed into parallel subchannels with CSI. Waterfilling solves the problem of optimal power allocation to these subchannels to achieve the rate approaching the channel capacity under total power constraint. In waterfilling, more power is alloted to good channels (high SNR) and less or no power to bad channels to increase the rate of good channels, resulting in channel capacity. Waterfilling finds the exact water level satisfying the power constraint employing an iterative algorithm to estimate and update the water level. In this process computation of partial sums of inverse of square of subchannel gain is repeatedly required. In this paper we reduced the computation time of waterfilling algorithm by replacing the partial sum computation with reference to an array which contains the precomputed partial sums in initialization phase.

Conversely to the fully connected case, it has been proved in theory that interference alignment (IA) can be achievable in a partially connected multi-cell multiple input and multiple output (MIMO) interfering broadcast channels (IBC) network of arbitrary size efficiently. For this applicable significance, based on the L-interfering MIMO IBC model, we present three iterative IA algorithms to solve the alignment problem for this type of model in this paper. Then we discuss the feasibility conditions and the computational complexity of the algorithms. Simulations show that, with a finite antenna number per transmitter and receiver pair between base station (BS) and user, the proposed algorithms can achieve the optimal degrees of freedom (DoF) and can be applied to a partially connected MIMO IBC network with arbitrary number of cells and users per cell.

In heterogeneous networks, the issue of interference between femtocells and macrocells should be carefully considered. Resource allocation schemes with cognitive technologies have been a key challenge to manage interference. In this paper, we investigate price-based power allocation strategies with the energy efficiency criterion for a spectrum-sharing heterogeneous cognitive network from the aspect of energy efficiency, and provide the utility function of macrocell and femtocells based on a non-cooperative Stackleberg game model. We build a combination of price vector and power allocation values by standard Lagrangian method and propose an improved iteration algorithm based on price updating to obtain the Stackleberg equilibrium solution. The simulation results verify the proposed method can improve energy efficiency and achieve better utility.

Optimization problems, variational inequalities, minimax problems can be formulated as equilibrium problems. The iterative algorithms of fixed points are often applied to finding the solution of equilibrium problems. In this paper, we introduce a new hybrid iterative algorithm for finding a common element of the set of fixed points of asymptotically nonexpansive mappings and the set of solutions of an equilibrium problem in Hilbert spaces. Besides, an example of variational inequality problem is given to illustrate the efficiency and performance of the newly algorithm.

The images of different areas require different test results on the edge. In this paper, the images of red blood cells in medical field need to be detected theirs cell size, roundness, number and other features. For the test requirements, an image edge detection algorithm is proposed based on improved Canny operator. This algorithm calculates the optimal high and low dual-threshold by using iteration arithmetic, and uses mathematical morphology to thin the detected images. The results proved that this algorithm can effectively reduce interference and noise edge and make more prominent detection characteristics, which is good ready for subsequent processing of the image.

This paper models image deconvolution as an l2-l1 minimization problem, which is an approach taken by many state-of-the-art image deconvolution algorithms. We present a novel iterative algorithm based on the split Bregman method and the stationary second-degree method, which efficiently addresses the classic convex minimization problem. The split Bregman method, which has been proven to be very efficient for non-differentiable minimization problems, decomposes the equivalent constrained version of the l2-l1 deconvolution problem into a series of sub-problems. These sub-problems are then individually solved using appropriate methods to obtain their closed-form solutions. Unlike the majority of other similar deconvolution algorithms, we use a modified stationary second-degree method to solve the l2-l1 denoising sub-problem, prompted by some recent work on the improvement of the iterative thresholding method. The presented algorithm can be categorized as a split Bregman method, so convergence of the solution can be guaranteed. In our experiment, the presented algorithm and the algorithms in references [6] and [8] are used to restore Gaussian-blurry and uniform-blurry images. The experimental results show that the presented algorithm is effective and it outperforms other algorithms in comparison.

According to the limitations of a single measurement algorithm in the current 3D models’ viewpoint extraction, this essay puts forward a viewpoint extraction algorithm based on AdaBoost iterative algorithm, which can make the features adaptive automatically. It, firstly, extracts 3D models’ feature descriptor and feature vector in the model library and adopts AdaBoost iterative algorithm to establish rules about classification and matching from geometric features and various viewpoint extraction algorithm; then, it constructs decision classifier in order to extract optimal viewpoint. In query process, the model obtains viewpoint extraction algorithm which can suit its geometric feature through decision classifier and then gets its best view by calculation. The experimental result shows this algorithm extraction effect is superior to the one by a single measurement algorithm.

In order to realize location in cellular networks, the location model with uniform noise based on AOA is established when seven base stations are available. Then a maximum likelihood estimation (MLE) method is proposed and realized by an iterative algorithm under this model. Finally, the simulation result of the iterative algorithm as compared to least squares estimation(LSE) algorithm under this AOA model is shown that the iterative algorithm is less error and more robust to noise disturbance than LSE algorithm.

Due to the wide application of range-based location algorithm for received signal strength, and according to the requirements of high accuracy and low power cost in the location algorithm for WSNs, in this paper, a Bayesian optimization RSSI and an indoor location algorithm for ILS were introduced by setting RRS ranging as location framework. Firstly, through analyzing the RSSI-based ranging model, an indoor location model was introduced. Secondly, in view of the influence on RSSI value caused by the indoor environment,the Bayesian probabilistic model was adopted to process the RSSI measured value and to screen out the "big probability" of RSSI value. Thirdly, Obtaining accurate measured data by estimating distance using method of minimum mean square error. Finally, Estimating the node location using least square method, and according to the TelosB node of Telos Series produced by company Crossbow, the ranging experiment can be designed and thus groups of experimental data were obtained and analyzed..The experimental results showed that the proposed location project greatly increased the location accuracy and decreased the computation complexity, and has obviously more advantage of running time over other location projects.

Stencils are finite-difference algorithms for solving large-scale and high-dimension partial differential equations. Due to the data dependences among the iterative statements in Stencils, traditional Stencil computations are be executed serially, rather than in parallel. It’s challenging to design an effective and scalable Stencil parallelized method. To address the issue of 3D data space computing, we present a serial execution model based on multi-layers symmetric Stencil method and time skewing techniques. Within this model, the iteration space is divided to multiple tiles based on time skewing, where the executive process is ordered by the sequence of tiles, and the nodes in each individual tile can be swept repeatedly to improve the data locality. In addition, we propose a novel 3D iterative space alternate tiling Stencil parallel method, which subdivides the iteration space along high dimension, and changes the execution sequence of tiles to reduce the data dependency and communication cost, where the partial order of tiles is still guaranteed. Experimental results demonstrate our proposed alternative tiling parallel method achieves better parallel efficiency and scalability compared with the domain-decomposition methods.

Fingerprint Identification System performance largely depends on the quality of input fingerprint images. High quality input fingerprint images are prerequisite for high performance systems. In this paper, we have proposed an algorithm of fingerprint image enhancement by using Iterative Fast Fourier Transform (IFFT). Iterative image reconstruction algorithms play an important role in fingerprint identification systems in order to achieve higher degree of efficiency. With the fast increase of the sizes of the fingerprint data, design of the reconstruction algorithms is of great importance in order to improve the performance. Fourier-based frequency orientation methods have the potential to considerably reduce the computation time in iterative reconstruction. We also have designed an approach for removing the false minutia generated during the fingerprint processing and a method to reduce the false minutia to increase the efficacy of identification system. We have used fingerprint Verification Competition 2006 (FVC 2006) as a database for implementation of proposed algorithm to verify the degree of efficiency of proposed algorithm. Experimental result shows that the proposed enhancement algorithm is better than existing Fast Fourier Transform algorithm.

This research considers a low carbon scheduling problem in unrelated parallel machines. To solve this problem, we first establish a low carbon scheduling mathematical model. Then an iterative ant optimization algorithm is presented. Furthermore, parameters of proposed iterative ant optimization algorithm are selected by Taguchi methods on generating test dataset. Finally, comparative experiments indicate the proposed iterative ant optimization algorithm has better performance on minimizing energy consumption as well as total tardiness.

Biological tissues have electrical conductivity and permittivity properties which depend on tissue composition, structure and health status. Bioelectrical properties can be utilized for non invasive disease diagnosis. Electrical Impedance Tomography (EIT) is a method for reconstructing the image of distribution of electrical conductivity and permittivity inside a volume from measurements made at the surface of the volume. EIT image reconstruction is an ill-posed problem that requires a priori information called regularization. The Total Variation (TV) regularization is often used in solving EIT inverse problem. In this paper, simulation has been carried out in noise free and noisy cases and TV regularized iterative Primal Dual Interior Point Method (PD-IPM) has been used to reconstruct the difference conductivity image.