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We present a collaborative attempt to build select toolboxes of Scilab using external Free and Open Source Software (FOSS) libraries. A C/C++ interface is written for each library. Scilab variables are transferred to C/C++ variables, computations carried out through one or more function calls and variables returned to Scilab. State of the art libraries, such as Octave, COIN-OR, OpenCV and IT++ have...
This paper investigates resource allocation algorithms that use limited communication - where the supplier of a resource broadcasts a coordinating signal using one bit of information to users per iteration. Rather than relay anticipated consumption to the supplier, the users locally compute their allocation, while the supplier measures the total resource consumption. Since the users do not compare...
Rectangular packing problem is common in the manufacturing industry. It is essentially a NP hard combinational optimization problem. In this paper, a novel algorithm based on dynamic updating of packing point set is proposed to deal with rectangular packing problem. In the process of packing, we update the packing point set dynamically. After placing a part, we need remove the packing point that has...
The traditional approach of teaching programming courses is teachers centric where students are passive learners. Also for such courses, the laboratory and classes are conducted separately. This paper focuses on integrating classroom and laboratory with hands-on for programming course. This approach is student centric which brings in active learning. However it has been less researched area and adequate...
This paper studies the problem of how to efficiently minimize network coding resource. A modified particle swarm optimization (PSO) algorithm is proposed to tackle the problem, with the concept of path-relinking (PR) integrated into the evolutionary framework. As an efficient local search heuristic that makes use of problem-specific domain knowledge, PR helps strike a better balance between global...
With the continuous development of computer science and control science, the complexity of the system also increased rapidly. Accordingly, people began to improve the security and stability of the systems, and fault diagnosis in time is an effectively method to reduce the loss of property. The reality systems are invariably more complex, nonlinear and non-Gaussian. The previous method cannot solve...
Nonorthogonal multiple access (NOMA) transmission is a popular candidate technology for the next generation broadband mobile communication systems because of high spectrum efficiency. In this paper, we proposed two beamforming scheme, namely, matched-to-the-stronger-channel (MSC) beamforming and optimal beamforming schemes for a two-user downlink NOMA system. Moreover, we also develop optimal power...
Error Correcting Output Coding (ECOC) is a multi-class classification technique in which multiple binary classifiers are trained according to a preset code matrix such that each one learns a separate dichotomy of the classes. While ECOC is one of the best solutions for multi-class problems, one issue which makes it suboptimal is that the training of the base classifiers is done independently of the...
Recently, Approximate Nearest Neighbor (ANN) Search has become a very popular approach for similarity search on large-scale datasets. In this paper, we propose a novel vector quantization method for ANN, which introduces a joint multi-layer K-Means clustering solution for determination of the codebooks. The performance of the proposed method is improved further by a joint encoding scheme. Experimental...
The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant of self-learning can be derived by applying block coordinate descent to two related but slightly different...
Due to asynchronous packet transmissions and rate mismatch among different coding flows in existing opportunistic network coding architectures, such as COPE, the amount of packets which can be coded together may be insufficient and thus the performance gain of network coding cannot be fully exploited. One feasible solution for this problem is to make coding nodepurposely delay some packet transmissions...
A novel sparse coding framework with unity range codes and the possibility to produce a discriminative dictionary is presented. The framework is, in contrast to many other works, able to handle unsupervised, supervised and semi-supervised settings. Furthermore, codes are constrained to be in unity range, which is beneficial in many scenarios. The paper presents the framework and solvers used to produce...
Coding efficiency can be enhanced through rate-distortion optimization (RDO) that provides a trade-off between bit-rate and distortion. In this paper, we have proposed Structural SIMilarity (SSIM) based RDO for 3D video coding improvement. SSIM index is a quality metric that gives better approximation to visual quality. Most of the existing literature on 3D video coding employs sum-of-squared error...
This research proposed a method for adaptive Lagrange multiplier determination for rate-distortion optimization with dynamic texture in High Efficiency Video Coding (HEVC). Inspired by the experimental results of the Lagrange multiplier selection test, the presented approach adaptively predicts the optimum Lagrange multiplier for different dynamic texture sequences, based on the features of the dynamic...
UnionPay's inter-bank transaction settlement platform (ITSP) generates a huge amount of bankcard transaction data everyday, recording different bankcard activities. In order to unleash the business value of these data, UnionPay has built a customized data warehouse based on Hadoop to manage and query the massive data imported from ITSP. However, the original system suffers from low storage utilization...
Motivated by real applications, heterogeneous learning has emerged as an important research area, which aims to model the co-existence of multiple types of heterogeneity. In this paper, we propose a HEterogeneous REpresentation learning model with structured Sparsity regularization (HERES) to learn from multiple types of heterogeneity. HERES aims to leverage two kinds of information to build a robust...
According to the characteristics of assembly line balancing problem, a modified honey bees mating optimization (MHBMO) algorithm was proposed to solve this problem. In this algorithm, the precedence matrix of tasks is proposed to apply the coding method based on the sequence of tasks. The initial feasible population was generated by a binary tree method, and a modified crossover operator and three...
Abstract-We consider the problem of designing constellation sets for multistage systems for high spectral efficiencies. In particular we will focus on two-stage systems where the cascaded decoding stages are associated to three types of receivers with different complexities and performances. The optimal receiver (S) processes the symbol LLR and delivers the maximum possible throughput, the simpler...
The nearest neighbor classifiers are popular supervised classifiers due to their ease of use and good performance. However, in spite of their success, they suffer from some defects such as high storage requirements, high computational complexity, and low noise tolerance. In order to address these drawbacks, prototype selection has been studied as a technique to reduce the size of training datasets...
Clustering, the process of grouping unlabelled data, is an important task in data analysis. It is regarded as one of the most difficult tasks due to the large search space that must be explored. Feature selection is commonly used to reduce the size of a search space, and evolutionary computation (EC) is a group of techniques which are known to give good solutions to difficult problems such as clustering...
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