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Quarterly published in print and online "Inventi Impact: Algorithm" publishes high quality unpublished as well as high impact pre-published research and reviews catering to the needs of researchers and professionals. The journal covers all the advances in the growing field of algorithms. Articles pertaining to following areas are particularly welcome: algorithm engineering; algorithmic graph theory; algorithms for databases and database design; algorithms for language processing; algorithms in biology, chemistry, physics, etc; approximation algorithms; design and analysis of algorithms; experimental algorithms: implementation and testing of algorithms; randomized algorithms; sorting and search algorithms etc.
This paper presents a novel approach, based on the whale optimization algorithm (WOA), for channel estimation in wireless
communication systems. The proposed method provides a means to accurately estimate the wireless channel, while not
requiring the statistical characteristics of the channel. This method uses the WOA to search for the best channel statistical
characteristics toward the ultimate goal of having the smallest bit error rate (BER). The proposed approach is aimed at
enhancing the efficiency of pilot-based OFDM systems under frequency-selective fading channels used in the performance
testing of 5G New Radio gNodeB. In terms of BER and mean square error (MSE), the performance of the proposed WOAbased
channel estimation algorithm is evaluated and compared with the conventional least square (LS) and minimum mean
square error (MMSE) algorithms. The simulation results demonstrate that the proposed algorithm provides highly competitive
performance over the MMSE algorithm and significantly outperforms the LS algorithm in a variety of system configurations.
Since the requirement on prior channel statistics information makes the MMSE algorithm impractical or extremely complex,
the proposed WOA-based channel estimation algorithm should be a suitable and promising candidate for dealing with channel
estimation problems. The simulation framework is implemented in MATLAB and available upon request....
In the pursuit of finding subclasses of the makespan minimization problem on unrelated\nparallel machines that have approximation algorithms with approximation ratio better than 2, the\ngraph balancing problem has been of current interest. In the graph balancing problem each job can\nbe non-preemptively scheduled on one of at most two machines with the same processing time on\neither machine. Recently, Ebenlendr, KrÃ?â?¡cÃ?Â¡l, and Sgall (Algorithmica 2014, 68, 62Ã¢â?¬â??80.) presented a\n7/4-approximation algorithm for the graph balancing problem. Let r, s Ã¢Ë?Ë? Z+. In this paper we\nconsider the graph balancing problem with two weights, where a job either takes r time units or\ns time units. We present a 3/2-approximation algorithm for this problem. This is an improvement\nover the previously best-known approximation algorithm for the problem with approximation\nratio 1.652 and it matches the best known inapproximability bound for it....
The Continuous Wavelet Transform (CWT) is an important mathematical tool in signal\nprocessing, which is a linear time-invariant operator with causality and stability for a fixed scale and\nreal-life application. A novel and simple proof of the FFT-based fast method of linear convolution is\npresented by exploiting the structures of circulant matrix. After introducing Equivalent Condition\nof Time-domain and Frequency-domain Algorithms of CWT, a class of algorithms for continuous\nwavelet transform are proposed and analyzed in this paper, which can cover the algorithms in\nJLAB andWaveLab, as well as the other existing methods such as the cwt function in the toolbox of\nMATLAB. In this framework, two theoretical issues for the computation of CWT are analyzed. Firstly,\nedge effect is easily handled by using Equivalent Condition of Time-domain and Frequency-domain\nAlgorithms of CWT and higher precision is expected. Secondly, due to the fact that linear convolution\nexpands the support of the signal, which parts of the linear convolution are just the coefficients of\nCWT is analyzed by exploring the relationship of the filters of Frequency-domain and Time-domain\nalgorithms, and some generalizations are given. Numerical experiments are presented to further\ndemonstrate our analyses....
The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which\nproves that considering the network topology of recommendation systems could help us to improve recommendation results.\nHowever, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an\nimportant role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements\nof collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and\na collaborative recommendation algorithm based on the bipartite community.Then we designed numerical experiments to verify\nthe algorithm validity under benchmark and real database....
Considering that the probability distribution of random variables in stochastic\nprogramming usually has incomplete information due to a perfect sample\ndata in many real applications, this paper discusses a class of two-stage stochastic\nprogramming problems modeling with maximum minimum expectation\ncompensation criterion (MaxEMin) under the probability distribution\nhaving linear partial information (LPI). In view of the nondifferentiability of\nthis kind of stochastic programming modeling, an improved complex algorithm\nis designed and analyzed. This algorithm can effectively solve the nondifferentiable\nstochastic programming problem under LPI through the variable\npolyhedron iteration. The calculation and discussion of numerical examples\nshow the effectiveness of the proposed algorithm....
Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge fromauxiliary domains.\nObviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate\neffectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative\nfiltering algorithm based on Feature Construction and LocallyWeighted Linear Regression (FCLWLR).We first construct features\nin different domains and use these features to represent different auxiliary domains.Thus the weight computation across different\ndomains can be converted as the weight computation across different features. Then we combine the features in the target domain\nand in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally,\nwe employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric\nregression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We\nconduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem\nby transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or crossdomain\nCF methods....
This paper is concerned with a generalization of the q-Bernstein polynomials and Stancu operators, where the function is evaluated at intervals which are in geometric progression. It is shown that these polynomials can be generated by a de Casteljau algorithm, which is a generalization of that relating to the classical case and q-Bernstein case....
Spatial crowdsourcing assigns location-related tasks to a group of workers (people equipped with smart devices and willing to\ncomplete the tasks), who complete the tasks according to their scope of work. Since space crowdsourcing usually requires workersâ??\nlocation information to be uploaded to the crowdsourcing server, it inevitably causes the privacy disclosure of workers. At the\nsame time, it is difficult to allocate tasks effectively in space crowdsourcing. Therefore, in order to improve the task allocation\nefficiency of spatial crowdsourcing in the case of large task quantity and improve the degree of privacy protection for workers, a\nnew algorithm is proposed in this paper, which can improve the efficiency of task allocation by disturbing the location of workers\nand task requesters through k-anonymity. Experiments show that the algorithm can improve the efficiency of task allocation\neffectively, reduce the task waiting time, improve the privacy of workers and task location, and improve the efficiency of space\ncrowdsourcing service when facing a large quantity of tasks....
For facility layout problem with continuous block and unequal area, it is key to generate feasible solution of facility layout with\narbitrary space form in order to find the optimal arrangement scheme under a given goal.According to the given slicing position and\nslicing mode, the plane for arrangement was divided into many block areas by use of plane segmentation, which was consistent with\nthe facilities in number.The precise coordinates of the lower-left corner and the top-right corner of each facility were calculated in\nlight of its area, width, and length. The corresponding algorithm was designed in the form of pseudocode.The procedure proposed\ncan provide a feasible facility layout solution. The running results of facilities layout instance containing 14 facilities show that the\nscheme can output facilities plane layout scheme quickly and provide decision support for the facilities planning....
When the measurement error of the external reference velocity changes dramatically, the traditional level damping for marine INS
needs to cut off the damping to maintain the navigation accuracy. The level channel has a large overshoot oscillation during the
variable damping instantaneous, which results in obvious position deviation. In order to solve this practical problem, a damping
model is established outside the INS. The most obvious advantage of the algorithm is that the damping algorithm does not affect
the inertial navigation solution. The fault-tolerant algorithm realizes the automatic damping switch according to the external
reference velocity error variation criterion, which avoids the velocity oscillation and position deviation. Compared with traditional
methods, the algorithm presented in this paper has higher reliability and better environmental adaptability. The effectiveness
of the algorithm is verified by the actual navigation test data....
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