Weighted multidimensional scaling (MDS) is an attractive technique for source localisation. This study considers the problem of locating the source using the time‐difference‐of‐arrival measurements in the presence of sensor position uncertainties. Recently, Cao et al . (2017) proposed a novel weighted MDS estimator and analytically proved its optimality on the source locating problem. However, two important equations, from Cao et al . (2017), in the verification of the optimality were incomplete and inadequately proved because of the Moore–Penrose pseudo‐inverse operation. This study presents two fundamental corollaries in the MDS‐based source localisation and then gives an analytical proof thoroughly. The two fundamental corollaries are necessary to support the optimality verification of the MDS estimators in the presence and absence of sensor position uncertainties, respectively. Different from the above two equations, the authors’ corollaries are established with no requirement about approximations with small measurement noises or sensor position uncertainties either. The theoretical derivation is corroborated by numerical examples.

Spectrum sensing at low signal-to-noise ratio regimes is a challenging area that has recently attracted much interest from the research community. In this letter, two novel detectors for weak signals are presented, the phase-rotated spectral correlation detector and the complex-decomposition-based PSCD, by means of exploiting the linear relationship between samples from two sampling channels. False alarm and detection probabilities of the proposed detectors are found based on statistical theory. Detection performance metrics of the proposed detectors in terms of the deflection coefficient and receiver operating characteristic are evaluated in additive white Gaussian noise channels. Analytical and simulated results indicate that the proposed detectors are blind detection techniques and outperform the energy detector and single-cycle cyclostationary detector.

Mobile cloud computing has become an emerging computing paradigm to extend the capability of the mobile devices and it has gained increasing popularity in recent years. Existing studies mainly focus on how to leverage the computing capability of the individual device by employing the capability from remote cloud datacenters or local mobile cloud formed by nearby devices. Different from these studies, we investigate how to improve the performance of data sharing in the peer-to-peer mobile cloud, with the limited bandwidth and the presence of dynamic and unpredictable wireless channel state. Specifically, we first formulate the data transmission among devices as a utility maximization problem with the consideration of limited bandwidth, incentive participation and the QoE (Quality of Experience) heterogeneity, based on incorporating publish/subscribe component into the base station. Then, a dynamic online algorithm, which does not need the future context (e.g., channel state) of the mobile cloud, is developed to simultaneously make the decision of data transmission and communication interface selection. Rigorously theoretical analysis shows the optimality and the effectiveness of the proposed algorithm. Extensive experiments are conducted to verify the analysis results and the superiority of the proposed algorithm over existing strategies.

Periodicity is one of the most common phenomena in the physical world. The problem of periodicity analysis (or period detection) is a research topic in several areas, such as signal processing and data mining. However, period detection is a very challenging problem, due to the sparsity and noisiness of observational datasets of periodic events. This paper focuses on the problem of period detection from sparse and noisy observational datasets. To solve the problem, a novel method based on the approximate greatest common divisor (AGCD) is proposed. The proposed method is robust to sparseness and noise, and is efficient. Moreover, unlike most existing methods, it does not need prior knowledge of the rough range of the period. To evaluate the accuracy and efficiency of the proposed method, comprehensive experiments on synthetic data are conducted. Experimental results show that our method can yield highly accurate results with small datasets, is more robust to sparseness and noise, and is less sensitive to the magnitude of period than compared methods.

An effective algorithm for removing impulse noise from corrupted images is presented under the framework of switching median filtering. Firstly, noisy pixels are distinguished by Local Outlier Factor incorporating with Boundary Discriminative Noise Detection (LOFBDND). Then, the directional weighted median filter is adopted to remove the detected impulses by replacing each noisy pixel with the weighted mean of its neighbors in the filtering window. Our noise detection algorithm makes the decision so accurate that the miss detection rate and false detection rate are very low. Extensive simulation results show that our method provides better performance in terms of PSNR and MAE than many other median filters for impulse noise removal.

This paper discusses two classes of symmetric Boolean functions. For each class, a necessary and sufficient condition for having optimum algebraic immunity is proposed. The algebraic degree and nonlinearity of the Boolean functions are also completely determined. And then we prove several of Braeken's conjectures about the algebraic degree and nonlinearity of the Boolean functions with optimum algebraic immunity in the two classes.

This paper provides a fast algorithm for Gröbner bases of ideals of F[x, y] over a field F. We show that only the S-polynomials of neighbor pairs of a strictly ordered finite generating set are needed in the computing of a Gröbner bases of the ideal. It reduces dramatically the number of unnecessary S-polynomials that are processed. Although the complexity of the algorithm is hard to evaluated, it obviously has a great improvement from Buchberger's Algorithm.

Multiple performance guarantees are increasingly needed in QoS-aware real-time applications. Unfortunately, most traditional scheduling algorithms only consider one or two dimensions of them. In this paper, we build a new scheduler model that is suitable for providing multiple services. Based on the scheduler mode, we propose a novel multi-dimensional scheduling scheme MDSS, which tasks timing constraints, QoS, throughput, fairness and load balancing into account. The MDSS includes three steps. Step 1 is used to provide real-time scheduling and the existing DASAP algorithm is employed in step 1. In step 2, we present a novel algorithm MQFQ that strives to enhance the QoS levels of accepted tasks and makes these tasks have fair QoS services, as well. Another new algorithm ITLB is proposed and used in step 3. The ITLB algorithms is capable of balancing load of nodes and improving throughput of the system. We have performed extensive simulation experiments to compare MDSS scheme with MDSR scheme, DASAP and DALAP algorithms. Experimental results indicate that MDSS improves QoS benefit, has better load balancing and has higher throughput over MDSR, DASAP and DALAP while guaranteeing high schedulability.

Synchronization attacks like random cropping and time-scale modification are very challenging problems to audio watermarking techniques. To combat these attacks, a novel content-dependent localized robust audio watermarking scheme is proposed. The basic idea is to first select steady high-energy local regions that represent music edges like note attacks, transitions or drum sounds by using different methods, then embed the watermark in these regions. Such regions are of great importance to the understanding of music and will not be changed much for maintaining high auditory quality. In this way, the embedded watermark has the potential to escape all kinds of distortions. Experimental results show strong robustness against common audio signal processing, time-domain synchronization attacks, and most distortions introduced in Stirmark for Audio.

This paper presents an algorithm for blind recognition of punctured convolutional codes with application for instance in adaptive systems. Minimal basic encoding matrices of the original convolutional codes and their puncturing patterns are determined according to the accounted parity check matrices of the punctured convolutional codes.

We give a series of constructions of correlation-immune function over finite fields. We prove that F/sub 2/ and F/sub 3/ are the only finite fields F/sub q/ with the property that every (n-1)th correlation-immune function in n>2 variables over F/sub q/ is linear. We also show that by choosing larger finite fields one can alleviate the tradeoff between the length of the linear equivalent and the order of correlation immunity. This is useful for the design of various cryptosystems.