In this work, a simple doped accumulator (DACC)-assisted relay system is proposed by using bit-interleaved coded modulation with iterative decoding (BICM-ID). An extrinsic information transfer (EXIT) chart analysis shows that DACC keeps the convergence tunnel of the EXIT curves open until almost the (1, 1) point of the mutual information, which avoids the error floor. In the relay system, errors may happen in the source-relay link (intra-link), however, they are allowed in our proposed technique where the correlation knowledge between the source and the relay is exploited at the destination node. Strong codes are not needed and even the systematic source bits can be simply extracted at the relay even though the systematic part may contain some errors. Hence, the complexity of the relay can be significantly reduced, and thereby the proposed system is energy-efficient. Furthermore, the error probability of the intra-link can be estimated at the receiver by utilizing the a posteriori log-likelihood ratios (LLRs) of the two decoders, and it can be further utilized in the iterative processing. Additionally, we provide the analysis of different relay location scenarios and compare the system performances by changing the relay's location. The transmission channels in this paper are assumed to suffer from additive white Gaussian noise (AWGN) and block Rayleigh fading. The theoretical background of this technique is the Slepian-Wolf/Shannon theorem for correlated source coding. The simulation results show that the bit-error-rate (BER) performances of the proposed system are very close to theoretical limits supported by the Slepian-Wolf/Shannon theorem.

In this paper, we consider a two-way multi-relay scenario and analyze the bit error rate (BER) and outage performance of an amplify-and-forward (AF) relaying protocol. We first investigate the bit error probability by considering channel estimation error. With the derivation of effective signal-to-noise ratio (SNR) at the transceiver and its probability density function (PDF), we can obtain a closed form formulation of the total average error probability of two-way multi-relay system. Furthermore, we also derive exact expressions of the outage probability for two-way relay through the aid of a modified Bessel function. Finally, numerical experiments are performed to verify the analytical results and show that our theoretical derivations are exactly matched with simulations.

This paper proposes a deep quasi-linear kernel for support vector machines (SVMs). The deep quasi-linear kernel can be constructed by using a pre-trained deep neural network. To realize this goal, a multilayer gated bilinear classifier is first designed to mimic the functionality of the pre-trained deep neural network, by generating the gate control signals using the deep neural network. Then, a deep quasi-linear kernel is derived by applying an SVM formulation to the multilayer gated bilinear classifier. In this way, we are able to further implicitly optimize the parameters of the multilayer gated bilinear classifier, which are a set of duplicate but independent parameters of the pre-trained deep neural network, by using an SVM optimization. Experimental results on different data sets show that SVMs with the proposed deep quasi-linear kernel have an ability to take advantage of the pre-trained deep neural networks and outperform SVMs with RBF kernels.

This paper proposes the test case prioritization in regression testing. The large size of a test suite to be executed in regression testing often causes large amount of testing cost. It is important to reduce the size of test cases according to prioritized test sequence. In this paper, we apply the Markov chain Monte Carlo random testing (MCMC-RT) scheme, which is a promising approach to effectively generate test cases in the framework of random testing. To apply MCMC-RT to the test case prioritization, we consider the coverage-based distance and develop the algorithm of the MCMC-RT test case prioritization using the coverage-based distance. Furthermore, the MCMC-RT test case prioritization technique is consistently comparable to coverage-based adaptive random testing (ART) prioritization techniques and involves much less time cost.

In this paper, we reformulate the issue related to wireless mesh networks (WMNs) from the Chief Executive Officer (CEO) problem viewpoint, and provide a practical solution to a simple case of the problem. It is well known that the CEO problem is a theoretical basis for sensor networks. The problem investigated in this paper is described as follows: an originator broadcasts its binary information sequence to several forwarding nodes (relays) over Binary Symmetric Channels (BSC); the originator's information sequence suffers from independent random binary errors; at the forwarding nodes, they just further interleave, encode the received bit sequence, and then forward it, without making heavy efforts for correcting errors that may occur in the originator-relay links, to the final destination (FD) over Additive White Gaussian Noise (AWGN) channels. Hence, this strategy reduces the complexity of the relay significantly. A joint iterative decoding technique at the FD is proposed by utilizing the knowledge of the correlation due to the errors occurring in the link between the originator and forwarding nodes (referred to as intra-link). The bit-error-rate (BER) performances show that the originator's information can be reconstructed at the FD even by using a very simple coding scheme. We provide BER performance comparison between joint decoding and separate decoding strategies. The simulation results show that excellent performance can be achieved by the proposed system. Furthermore, extrinsic information transfer (EXIT) chart analysis is performed to investigate convergence property of the proposed technique, with the aim of, in part, optimizing the code rate at the originator.

One of the most well-studied problems in data mining is computing the collection of frequent itemsets in large transactional databases. Since the introduction of the famous Apriori algorithm [14], many others have been proposed to find the frequent itemsets. Among such algorithms, the approach of mining closed itemsets has raised much interest in data mining community. The algorithms taking this approach include TITANIC [8], CLOSET+ [6], DCI-Closed [4], FCI-Stream [3], GC-Tree [5], TGC-Tree [16] etc. Among these algorithms, FCI-Stream, GC-Tree and TGC-Tree are online algorithms work under sliding window environments. By the performance evaluation in [16], GC-Tree [15] is the fastest one. In this paper, an improved algorithm based on GC-Tree is proposed, the computational complexity of which is proved to be a linear combination of the average transaction size and the average closed itemset size. The algorithm is based on the essential theorem presented in Sect. 4.2. Empirically, the new algorithm is several orders of magnitude faster than the state of art algorithm, GC-Tree.

Frequent Itemsets(FI) mining is a popular and important first step in analyzing datasets across a broad range of applications. There are two main problems with the traditional approach for finding frequent itemsets. Firstly, it may often derive an undesirably huge set of frequent itemsets and association rules. Secondly, it is vulnerable to noise. There are two approaches which have been proposed to address these problems individually. The first problem is addressed by the approach Frequent Closed Itemsets(FCI), FCI removes all the redundant information from the result and makes sure there is no information loss. The second problem is addressed by the approach Approximate Frequent Itemsets(AFI), AFI could identify and fix the noises in the datasets. Each of these two concepts has its own limitations, however, the authors find that if FCI and AFI are put together, they could help each other to overcome the limitations and amplify the advantages. The new integrated approach is termed Noise-tolerant Frequent Closed Itemset(NFCI). The results of the experiments demonstrate the advantages of the new approach: (1) It is noise tolerant. (2) The number of itemsets generated would be dramatically reduced with almost no information loss except for the noise and the infrequent patterns. (3) Hence, it is both time and space efficient. (4) No redundant information is in the result.

This paper proposes a so called quasi-linear support vector machine (SVM), which is an SVM with a composite quasi-linear kernel. In the quasi-linear SVM model, the nonlinear separation hyperplane is approximated by multiple local linear models with interpolation. Instead of building multiple local SVM models separately, the quasi-linear SVM realizes the multi local linear model approach in the kernel level. That is, it is built exactly in the same way as a single SVM model, by composing a quasi-linear kernel. A guided partitioning method is proposed to obtain the local partitions for the composition of quasi-linear kernel function. Experiment results on artificial data and benchmark datasets show that the proposed method is effective and improves classification performances.

A monolithic frequency synthesizer with wide tuning range, low phase noise and spurs was realized in 0.13,$mu$m CMOS technology. It consists of an analog PLL, a harmonic-rejection mixer and injection-locked frequency doublers to cover the whole 6--18,GHz frequency range. To achieve a low phase noise performance, a sub-sampling PLL with non-dividers was employed. The synthesizer can achieve phase noise $-$113.7,dBc/Hz@100,kHz in the best case and the reference spur is below $-$60,dBc. The core of the synthesizer consumes about 110,mA*1.2,V.

Practical, low complexity time of arrival (TOA) estimation method with high accuracy are attractive in ultra wideband (UWB) ranging and localization. In this paper, a generalized maximum likelihood energy detection (GML-ED) ranging method is proposed and implemented. It offers low complexity and can be applied in various environments. An error model is first introduced for TOA accuracy evaluation, by which the optimal integration interval can be determined. Aiming to suppress the significant error created by the false alarm events, multiple pulses are utilized for accuracy promotion at the cost of extra energy consumption. For this reason, an energy efficiency model is also proposed based on the transmitted pulse number. The performance of the analytical research is evaluated and verified through practical experiments in a typical indoor environment.

We thank Kamata et al. (2023) [1] for their interest in our work [2], and for providing an explanation of the quasi-linear kernel from a viewpoint of multiple kernel learning. In this letter, we first give a summary of the quasi-linear SVM. Then we provide a discussion on the novelty of quasi-linear kernels against multiple kernel learning. Finally, we explain the contributions of our work [2].