A novel algorithm associated with fractal preprocessors, wavelet feature extractors and unsupervised neural classifiers is proposed for detecting radar targets embedded in sea ice and sea clutter. Utilizing the advantages of fractals, wavelets and neural networks, the algorithm is suitable for real-time and automatic applications. Fractal preprocessor can increase 10 dB signal-to-clutter ratios (S/C) for radar images by using fractal error. Fractal error will make easy to detect radar targets embedded in high clutter environments. Wavelet feature extractors with a high speed computing architecture, can extract enough information for classifying radar targets and clutter, and improve signal-to-clutter ratios. Wavelet feature extractors can also provide flexible combinations for feature vectors at different clutter environments. The unsupervised neural classifier has a parallel operation architecture easily applied to hardware, and a low computational load algorithm without manual interventions during learning stage. We modified the unsupervised competitive learning algorithm to be applicable for detecting small radar targets by introducing an asymmetry neighborhood factor. The asymmetry neighborhood factor can provide a protective learning to prevent interference from clutter and improve the learning effects of radar targets. The small radar targets in Millimeter wave (MMW) and X-band radar images have been successfully discriminated by our proposed algorithm. The effective, efficient, high noise immunity characteristics for our proposed algorithm have been demonstrated to be suitable for automatic and real time applications.

Background: The applying of third-party libraries is an integral part of many applications. But the libraries choosing is time-consuming even for experienced developers. The automated recommendation system for libraries recommendation is widely researched to help developers to choose libraries. Aim: from software engineering aspect, our research aims to give developers a reliable recommended list of third-party libraries at the early phase of software development lifecycle to help them build their development environment faster; and from technical aspect, our research aims to build a generalizable recommendation system framework which combines collaborative filtering and topic modeling techniques, in order to improve the performance of libraries recommendation significantly. Our works on this research: 1) we design a hybrid methodology to combine collaborative filtering and LDA text mining technology; 2) we build a recommendation system framework successfully based on the above hybrid methodology; 3) we make a well-designed experiment to validate the methodology and framework which use the data of 1,013 mobile application projects; 4) we do the evaluation for the result of the experiment. Conclusions: 1) hybrid methodology with collaborative filtering and LDA can improve the performance of libraries recommendation significantly; 2) based on the hybrid methodology, the framework works very well on the libraries recommendation for helping developers' libraries choosing. Further research is necessary to improve the performance of the libraries recommendation including: 1) use more accurate NLP technologies improve the correlation analysis; 2) try other similarity calculation methodology for collaborative filtering to rise the accuracy; 3) on this research, we just bring the time-series approach to the framework and make an experiment as comparative trial, the result shows that the performance improves continuously, so in further research we plan to use time-series data-mining as the basic methodology to update the framework.

Compressive spectral imaging (CSI) systems capture the 3D spatiospectral data by measuring the 2D compressed focal plane array (FPA) coded projection with the help of reconstruction algorithms exploiting the sparsity of signals. However, the contradiction between the multi-dimension of the scenes and the limited dimension of the sensors has limited improvement of recovery performance. In order to solve the problem, a novel CSI system based on a coded aperture snapshot spectral imager, RGB-CASSI, is proposed, which has two branches, one for CASSI, another for RGB images. In addition, considering that conventional reconstruction algorithms lead to oversmoothing, a RGB-guided low-rank (RGBLR) method for compressive hyperspectral image reconstruction based on compressed sensing and coded aperture spectral imaging system is presented, in which the available additional RGB information is used to guide the reconstruction and a low-rank regularization for compressive sensing and a non-convex surrogate of the rank is also used instead of nuclear norm for seeking a preferable solution. Experiments show that the proposed algorithm performs better in both PSNR and subjective effects compared with other state-of-art methods.

MapReduce still suffers from a problem known as skew, where load is unevenly distributed among tasks. Existing solutions follow a similar pattern that estimates the load of each task and then rebalances the load among tasks. However, these solutions often incur heavy overhead due to the load estimation and rebalancing. In this paper, we present DynamicAdjust, a dynamic resource adjustment technique for mitigating skew in MapReduce. Instead of rebalancing the load among tasks, DynamicAdjust adjusts resources dynamically for the tasks that need more computation, thereby accelerating these tasks. Through experiments using real MapReduce workloads on a 21-node Hadoop cluster, we show that DynamicAdjust can effectively mitigate the skew and speed up the job completion time by up to 37.27% compared to the native Hadoop YARN.

In this letter, we propose an Adaptive Modulation and Coding (AMC) scheme with relay protocols, such as Amplify-and-Forward (AF), Decode-and-Forward (DF) and De-Modulate-and-Forward (DMF). We perform simulations based on 3GPP Long Term Evolution-Advanced (LTE-A) parameters to compare the performance of an adaptive Modulation and Coding Scheme (MCS) using relay protocols of AF, DF, and DMF with non-adaptive MCS, with the same relay protocols. We analyze the performance of the proposed scheme and observe how the proposed AMC scheme with DMF performs at various Signal to Noise Ratio (SNR) regions. The simulation results have shown that the performance of the proposed AMC scheme with relay protocols of DMF is much better at lower and a higher SNR regions and also provides higher average throughput.

This paper contributes to k-error linear complexity of some generalized cyclotomic binary sequences of length 2pm and pm constructed in recent years. By defining related reference sequences, we find that these sequences possess very low k-error linear complexity for some certain values of the parameter k even though they have high linear complexity. Moreover, we point out that (p-1)-tuple distributions of all these sequences are not span. Thus they should be selected carefully for use in stream cipher systems.

Objective: Detection of Electrocardiogram (ECG) characteristic points can provide critical diagnostic information about heart diseases. We proposed a novel feature extraction and machine learning scheme for automatic detection of ECG characteristic points. Methods: A new feature, termed as randomly selected wavelet transform (RSWT) feature, was devised to represent ECG characteristic points. A random forest classifier was adapted to infer the characteristic points position with high sensitivity and precision. Results: Compared with other state-of-the-art algorithms' testing results on QT database, our detection results of RSWT scheme showed comparable performance (similar sensitivity, precision, and detection error for each characteristic point). RSWT testing on MIT-BIH database also demonstrated promising cross-database performance. Conclusion: A novel RSWT feature and a new detection scheme was fabricated for ECG characteristic points. The RSWT demonstrated a robust and trustworthy feature for representing ECG morphologies. Significance: With the effectiveness of the proposed RSWT feature we presented a novel machine learning based scheme to automatically detect all types of ECG characteristic points at a time. Furthermore, it showed that our algorithm achieved better performance than other reported machine learning based methods.

A new method is developed to generate fuzzy rules from numerical data. This method consists of two algorithms: Algorithm 1 is used to identify structures of the given data set, that is, the optimal number of rules of system; Algorithm 2 is used to identify parameter of the used model. The former is belonged to unsupervised learning, and the latter is belonged to supervised learning. To identify parameters of fuzzy model, we developed a neural network which is referred to as Unsymmetrical Gaussian Function Network (UGFN). Unlike traditional fuzzy modelling methods, in the present method, a) the optimal number of rules (clusters) is determinde by input-output data pairs rather than by only output data as in sugeno's method, b) parameter identification of ghe present model is based on a like-RBF network rather than backpropagation algorithm. Our method is simple and effective because it integrates fuzzy logic with neural networks from basic network principles to neural architecture, thereby establishing an unifying framework for different fuzzy modelling methods such as one with cluster analysis or neural networks and so on.

Due to the discontinuity of packet based traffic, the user terminals in next generation mobile telecommunications systems will be equipped with sleep mode operation functions for power saving purpose. The sleep mode parameters should be appropriately configured so that power consumption can be sufficiently decreased while packet queue length and packet delay are restricted within a demanded level. This paper proposes an adaptive sleep mode parameter configuration scheme which is able to jointly optimize the inactivity timer and sleep period in response to the variation of user traffic arrival pattern. The optimization target of this scheme is to minimize mobile terminal power consumption while ensuring that the mean downlink packet queue length do not exceed a certain threshold. Results of computer simulations prove that, the presented approach perfectly manages packet queue length restriction, packet delay control and power saving in a wide range of user packet inter-arrival rates both in single- and dual-service scenarios.

In this letter we propose a new Whiteman generalized cyclotomic sequence of order 4. Meanwhile, we determine its linear complexity and minimal polynomial. The results show that this sequence possesses both high linear complexity and optimal balance on 1 s and 0 s, which may be attractive for cryptographic applications.

Bugs in industrial financial systems have not been extensively studied. To address this gap, we focused on the empirical study of bugs in three systems, PMS, β-Analyzer, and OrderPro. Results showed the 3 most common types of bugs in industrial financial systems to be internal interface (19.00%), algorithm/method (17.67%), and logic (15.00%).

In many wireless sensor applications, skyline monitoring queries that continuously retrieve the skyline objects as well as the complete set of nodes that reported them play an important role. This paper presents SKYMON, a novel energy-efficient monitoring approach. The basic idea is to prune nodes that cannot yield a skyline result at the sink, as indicated by their (error bounded) prediction values, to suppress unnecessary sensor updates. Every node is associated with a prediction model, which is maintained at both the node and the sink. Sensors check sensed data against model-predicted values and transmit prediction errors to the sink. A data representation scheme is then developed to calculate an approximate view of each node's reading based on prediction errors and prediction values, which facilitates safe node pruning at the sink. We also develop a piecewise linear prediction model to maximize the benefit of making the predictions. Our proposed approach returns the exact results, while deceasing the number of queried nodes and transferred data. Extensive simulation results show that SKYMON substantially outperforms the existing TAG-based approach and MINMAX approach in terms of energy consumption.

This letter provides an efficient massive multiple-input multiple-output (MIMO) detector based on quasi-newton methods to speed up the convergence performance under realistic scenarios, such as high user load and spatially correlated channels. The proposed method leverages the information of the Hessian matrix by merging Barzilai-Borwein method and Limited Memory-BFGS method. In addition, an efficient initial solution based on constellation mapping is proposed. The simulation results demonstrate that the proposed method diminishes performance loss to 0.7dB at the bit-error-rate of 10-2 at 128×32 antenna configuration with low complexity, which surpasses the state-of-the-art (SOTA) algorithms.

Cooperative relaying, while effective in mitigating fading effects, might reduce the overall network throughput since its overhead such as additional time slot and frequency band can be significant. In order to overcome this problem, this paper proposes a superposition coding based cooperative relay scheme to provide reliable transmission with little or no overhead. This scheme exploits the superimposed messages for users in the network to achieve the simultaneous transmission of two or more independent data streams. This scheme reduces the number of transmission phases to the same as that of conventional cooperative relay schemes. The symbol error performance of the proposed scheme is analyzed and simulated.

Based on the mutuality between arrival moments of uplink and downlink messages, this paper proposes a scheme that assigns different time-out thresholds for mobile terminal sleep mode operation according to the direction of the message just processed. Simulation results prove that, this approach can increase the power saving factor of a mobile terminal without degrading QoS.

This paper focuses on recovering from processor transient faults in pipelined multiprocessor systems. A pipelined machine may employ out of order execution and branch prediction techniques to increase performance, thus a precise computation state would not be available. We propose an efficient scheme to maintain the precise computation state in a pipelined machine. The goal of this paper is to implement checkpointing and rollback recovery utilizing the technique of precise interrupt in a pipelined system. Detailed analysis is included to demonstrate the effectiveness of this method.

In this paper, we propose a novel fault-tolerant multicast algorithm for n-dimensional wormhole routed hypercubes. The multicast algorithm will remain functional if the number of faulty nodes in an n-dimensional hypercube is less than n. Multicast is the delivery of the same message from one source node to an arbitrary number of destination nodes. Recently, wormhole routing has become one of the most popular switching techniques in new generation multicomputers. Previous researches have focused on fault-tolerant one-to-one routing algorithms for n-dimensional meshes. However, little research has been done on fault-tolerant one-to-many (multicast) routing algorithms due to the difficulty in achieving deadlock-free routing on faulty networks. We will develop such an algorithm for faulty hypercubes. Our approach is not based on adding physical or virtual channels to the network topology. Instead, we integrate several techniques such as partitioning of nodes, partitioning of channels, node label assignments, and dual-path multicast to achieve fault tolerance. Both theoretical analysis and simulation are performed to demonstrate the effectiveness of the proposed algorithm.

This paper investigates two issues of cellular engineering for cellular systems enhanced with two-hop fixed relay nodes (FRNs): spectrum partitioning and relay positioning, under the assumption of frequency reuse distance being equal to one. A channel-dependent spectrum partitioning scheme is proposed. According to this scheme, the ensemble mean of signal-to-interference-ratio on respective sets of links are taken into account to determine the bandwidths assigned to links connecting base station (BS) and FRNs, those connecting FRNs and mobile terminals (MTs) and those connecting BS and MTs. The proper FRN positioning is formulated as a constraint optimization problem, which tries to maximize the mean user data rate while at the same time ensures in probability 95% users being better served than in conventional cellular systems without relaying. It is demonstrated with computer simulations that FRN positioning has a strong impact on system performance. In addition, when FRNs can communicate with BS over line-of-sight channels the FRN enhanced cellular system with our proposed spectrum partitioning can remarkably outperform that with a known channel-borrowing based scheme and the conventional cellular systems without relaying. Simulation results also show that with proper FRN positioning the proposed spectrum partitioning scheme is robust against the unreliability of links connecting BS and FRNs.

A Hyper-Universal Switch Box (HUSB) [1]-[3] can yield a feasible (detailed) routing solution for any given routing requirement of multi-pin nets or multi-point connections of surrounding terminals. This flexible routing structure obviously possesses multiple potential applications for re-configurable systems such as FPGAs and communication switching networks [4],[5]. Based on the same decomposition theory developed in the design scheme of such powerful switching structure, a simple routing algorithm can also be developed. The router is exact in terms of its assured capability in finding a routing solution, and it is efficient due to the divide and conquer nature and simple mapping scheme for pre-analyzed routing patterns saved in data base.

A large-range switchable RF signal generator is demonstrated using a triple-wavelength fiber laser with uneven-frequency-spacing. Due to the birefringence characteristics of the triple-wavelength fiber laser, switchable dual-wavelength operation can be obtained by adjusting a polarization controller. Therefore, we can achieve a stable RF signals at microwave or millimeter-wave band.