Positioning information plays a significant role in multi-unmanned aerial vehicles (UAVs) applications. Traditionally, the positioning information is widely provided by Global Navigation Satellite System (GNSS) due to its good performance and global coverage. However, owing to complicated flight environment or signal blockage, jamming and unintentional interference, the UAVs may fail to locate themselves by using GNSS alone. As a new method to resolve these problems, cooperative positioning, by incorporating peer-to-peer range measurements and assisted information, has attracted more and more attentions due to its ability to enhance the accuracy and availability of positioning. However, achieving good performance of cooperative positioning of multi-UAVs is challenging as their mobility, arbitrary nonlinear state-evolution, measurement models and limited computation and communication resources. In this paper, we present a factor graph (FG) representation and message passing methodology to solve cooperative positioning problem among UAVs in 3-dimensional environment where GNSS cannot provide services. Moreover, to deal with the nonlinear state-evolution and measurement models while decreasing the computation complexity and communication cost, we develop a distributed algorithm for dynamic and hybrid UAVs by means of Spherical-Radial Cubature Rules (CR) method with belief propagation (BP) and variational message passing (VMP) methods (CRBP-VMP) on the FG. The proposed CRBP deals with the highly non-linear state-evolution models and non-Gaussian distributions, the VMP method is employed for ranging message, gets the simpler message representation and can reduce communication cost in the joint estimation problem. Simulation results demonstrate that the higher positioning accuracy, the better convergence as well as low computational complexity and communication cost of the proposed CRBP-VMP algorithm, which can be achieved compared with sum-product algorithm over a wireless network (SPAWN) and traditional Cubature Kalman Filters (CKF) method.

Fault prediction aims to identify whether a software module is defect-prone or not according to metrics that are mined from software projects. These metric values, also known as features, may involve irrelevance and redundancy, which hurt the performance of fault prediction models. In order to filter out irrelevant and redundant features, a Hybrid Feature Selection (abbreviated as HFS) method for software fault prediction is proposed. The proposed HFS method consists of two major stages. First, HFS groups features with hierarchical agglomerative clustering; second, HFS selects the most valuable features from each cluster to remove irrelevant and redundant ones based on two wrapper based strategies. The empirical evaluation was conducted on 11 widely-studied NASA projects, using three different classifiers with four performance metrics (precision, recall, F-measure, and AUC). Comparison with six filter-based feature selection methods demonstrates that HFS achieves higher average F-measure and AUC values. Compared with two classic wrapper feature selection methods, HFS can obtain a competitive prediction performance in terms of average AUC while significantly reducing the computation cost of the wrapper process.

Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness.

This paper analyzes the performance of various decoders in a two-user interference channel, and some improved decoders based on enhanced utilization of channel state information at the receiver side are presented. Further, new decoders, namely hierarchical constellation based decoders, are proposed. Simulations show that the improved decoders and the proposed decoders have much better performance than existing decoders. Moreover, the proposed decoders have lower decoding complexity than the traditional maximum likelihood decoder.

Under the condition of inadequate torque management, contact failure could occur in the interconnecting connector. Contact failure reduces the local immunity and degrades the electromagnetic properties of the equipment. It has been shown in previous reports that connector contact failure causes the parasitic inductance and radiated electromagnetic noise to increase. However, there is not enough discussion about the effects of connector torque fluctuation on the surrounding electromagnetic environment. Thus, in this study, the effects of a changing connector torque value on the circuit response and near field at the contact boundary were investigated. Based on these results, we discuss the influence of torque fluctuation on the electromagnetic environment surrounding the connector.

Previous works have studied the throughput allocation of the heterogeneous storage system consisting of SSD and HDD in the dynamic setting where users are not all present in the system simultaneously, but those researches make multiple servers as one large resource pool, and cannot cope with the multi-server environment. We design a dynamic throughput allocation mechanism named DAM, which can handle the throughput allocation of multiple heterogeneous servers in the dynamic setting, and can provide a number of desirable properties. The experimental results show that DAM can make one dynamic throughput allocation of multiple servers for making sure users' local allocations in each server, and can provide one efficient and fair throughput allocation in the whole system.

In the obnoxious facility game, we design mechanisms that output a location of an undesirable facility based on the locations of players reported by themselves. The benefit of a player is defined to be the distance between her location and the facility. A player may try to manipulate the output of the mechanism by strategically misreporting her location. We wish to design a λ-group strategy-proof mechanism i.e., for every group of players, at least one player in the group cannot gain strictly more than λ times her primary benefit by having the entire group change their reports simultaneously. In this paper, we design a k-candidate λ-group strategy-proof mechanism for the obnoxious facility game in the metric defined by k half lines with a common endpoint such that each candidate is a point in each of the half-lines at the same distance to the common endpoint as other candidates. Then, we show that the benefit ratio of the mechanism is at most 1+2/(k-1)λ. Finally, we prove that the bound is nearly tight.

Mining software artifacts is a useful way to understand the source code of software projects. Topic modeling in particular has been widely used to discover meaningful information from software artifacts. However, software artifacts are unstructured and contain a mix of textual types within the natural text. These software artifact characteristics worsen the performance of topic modeling. Among several natural language pre-processing tasks, removing stop words to reduce meaningless and uninteresting terms is an efficient way to improve the quality of topic models. Although many approaches are used to generate effective stop words, the lists are outdated or too general to apply to mining software artifacts. In addition, the performance of the topic model is sensitive to the datasets used in the training for each approach. To resolve these problems, we propose an automatic stop word generation approach for topic models of software artifacts. By measuring topic coherence among words in the topic using Pointwise Mutual Information (PMI), we added words with a low PMI score to our stop words list for every topic modeling loop. Through our experiment, we proved that our stop words list results in a higher performance of the topic model than lists from other approaches.

Electromagnetic relays were developed in the first half of 19th century. At the beginning, they have been mainly used for telecommunication systems, afterwards, their uses were expanded, they have been applied to various systems such as industry products, traffic control equipment, household appliances and so on. During this time, international standardization on them became active, Japan took part in the auxiliary relay committee in the International Electrotechnical Commission (IEC). Recently, Japan is playing an important role in the committee activities. In this paper, transition and the present circumstance on technical trends of the electromagnetic relays and their activities on international standardization are described, talking about some future prospects.

As the demand of data reliability becomes more and more larger, most of today's storage systems adopt erasure codes to assure the data could be reconstructed when suffering from physical device failures. In order to fast recover the lost data from a single failure, recovery optimization methods have attracted a lot of attention in recent years. However, most of the existing optimization methods focus on homogeneous devices, ignoring the fact that the storage devices are usually heterogeneous. In this paper, we propose a new recovery optimization method named HSR (Heterogeneous Storage Recovery) method, which uses both loads and speed rate among physical devices as the optimization target, in order to further improve the recovery performance for heterogeneous devices. The experiment results show that, compared to existing popular recovery optimization methods, HSR method gains much higher recovery speed over heterogeneous storage devices.

We realize a multicore erbium-doped fiber amplifier (MC-EDFA) with 2dB optical gain improvement (average) by recycling the residual 0.98μm pump light from the MC-EDF output. Eight-channel per core wavelength division multiplexed (WDM) Nyquist PM-16QAM optical signal amplification is demonstrated over a 40-minute period. Furthermore, we demonstrate the proposed MC-EDFA's stability by using it to amplify a Nyquist PM-16QAM signal and evaluating the resulting Q-factor variation. We found that our scheme contributes to reducing the total power consumption of MC-EDFAs in spatial division multiplexing (SDM)/WDM networks by up to 33.5%.

The traditional recommendation system (RS) can learn the potential personal preferences of users and potential attribute characteristics of items through the rating records between users and items to make recommendations.However, for the new items with no historical rating records,the traditional RS usually suffers from the typical cold start problem. Additional auxiliary information has usually been used in the item cold start recommendation,we further bring temporal dynamics,text and relevance in our models to release item cold start.Two new cold start recommendation models TmTx(Time,Text) and TmTI(Time,Text,Item correlation) proposed to solve the item cold start problem for different cold start scenarios.While well-known methods like TimeSVD++ and CoFactor partially take temporal dynamics,comments,and item correlations into consideration to solve the cold start problem but none of them combines these information together.Two models proposed in this paper fused features such as time,text,and relevance can effectively improve the performance under item cold start.We select the convolutional neural network (CNN) to extract features from item description text which provides the model the ability to deal with cold start items.Both proposed models can effectively improve the performance with item cold start.Experimental results on three real-world data set show that our proposed models lead to significant improvement compared with the baseline methods.

Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.

This paper describes transmission line couplers for non-contact connecters. Their characteristics are formulated in closed forms and design methodologies are presented. As their applications, three different types of transmission line couplers, two-fold transmission line coupler, single-ended to differential conversion transmission line coupler, and rotatable transmission line coupler are reviewed.

Adaptive voltage scaling is a promising approach to overcome manufacturing variability, dynamic environmental fluctuation, and aging. This paper focuses on error prediction based adaptive voltage scaling (EP-AVS) and proposes a mean time to failure (MTTF) aware design methodology for EP-AVS circuits. Main contributions of this work include (1) optimization of both voltage-scaled circuit and voltage control logic, and (2) quantitative evaluation of power saving for practically long MTTF. Experimental results show that the proposed EP-AVS design methodology achieves 38.0% power saving while satisfying given target MTTF.

With the existing of coverage holes, the Quality of Service (such as event response, package delay, and the life time et al.) of a Wireless Sensor Network (WSN) may become weaker. In order to recover the holes, one can locate them by identifying the boundary nodes on their edges. Little effort has been made to distinguish the boundary nodes in a model where wireless sensors are randomly deployed on a three-dimensional surface. In this paper, we propose a distributed method which contains three steps in succession. It first projects the 1-hop neighborhood of a sensor to the plane. Then, it sorts the projected nodes according to their angles and finds out if there exists any ring formed by them. At last, the algorithm validates a circle to confirm that it is a ring surrounding the node. Our solution simulates the behavior of rotating a semicircle plate around a sensor under the guidance of its neighbors. Different from the existing results, our method transforms a three-dimensional problem into a two-dimensional one and maintaining its original topology, and it does not rely on any complex Hamiltonian Cycle finding to test the existence of a circle in the neighborhood of a sensor. Simulation results show our method outperforms others at the correctness and effectiveness in identifying the nodes on the edges of a three-dimensional WSN.

In this paper, we show exact bit error rates (BERs) for orthogonal space-time block code (OSTBC) decoded-and-forward (DF) relaying networks over independent and non-identically distributed (INID) Rayleigh fading channels. We consider both non-adaptive DF (non-ADF) and adaptive DF (ADF) schemes for OSTBC relay networks with arbitrary multiple-input multiple-output (MIMO) relay antenna configurations. For each scheme, we derive the probability density functions (PDFs) of indirect link and combined links, respectively. Based on the derived PDFs, we express exact BERs and then, their accuracy is verified by the comparison with simulation results. It is confirmed that the transmit diversity gain of the relay node can be obtained when the relay is close to the source and then, the receive diversity gain of the relay node as well as ADF gain over non-ADF can be obtained when the relay is close to the destination.

Rotation symmetric Boolean functions which are invariant under the action of cyclic group have been used in many different cryptosystems. This paper presents a new construction of balanced odd-variable rotation symmetric Boolean functions with optimum algebraic immunity. It is checked that, at least for some small variables, such functions have very good behavior against fast algebraic attacks. Compared with some known rotation symmetric Boolean functions with optimum algebraic immunity, the new construction has really better nonlinearity. Further, the algebraic degree of the constructed functions is also high enough.

The electric properties of a field-effect transistor not only depend on gate surface sidewall but also on channel orientation when applying channel stain engineering. The change of the gate surface and channel orientation through the rotated FinFETs provides the capability to compare the orientation dependence of performance and reliability. This study characterized the <100> and <110> channels of FinFETs on the same wafer under tensile and compressive stresses by cutting the wafer into rectangular silicon pieces and evaluated their piezoresistance coefficients. The piezoresistance coefficients of the <100> and <110> silicon under tensile and compressive stresses were first evaluated based on the current setup. Tensile stresses enhance the mobilities of both <100> and <110> channels, whereas compressive stresses degrade them. Electrical characterization revealed that the threshold voltage variation and drive current degradation of the {100} surface were significantly higher than those of {110} for positive bias temperature instability and hot carrier injection with equal gate and drain voltage (VG=VD). By contrast, insignificant difference is noted for the subthreshold slope degradation. These findings imply that a higher ratio of bulk defect trapping is generated by gate voltage on the <100> surface than that on the <110> surface.

A multi-carrier and blind shift-frequency jamming(MCBSFJ) against the pulsed compression radar with order-statistic (OS) constant false alarm rate (CFAR) detector is proposed. Firstly, according to the detection principle of the OS-CFAR detector, the design requirements for jamming signals are proposed. Then, some key parameters of the jamming are derived based on the characteristics of the OS-CFAR detector. As a result, multiple false targets around the real target with the quantity, amplitude and space distribution which can be controlled are produced. The simulation results show that the jamming method can reduce the detection probability of the target effectively.