Let Fpm be the field of pm elements where p is an odd prime. In this letter, binary sequence pairs of period N=pm-1 are presented, where sequences are generated from the polynomial x2-c for any c Fpm{0}. The cross-correlation values of sequence pairs are completely determined, our results show that those binary sequence pairs have optimal three-level correlation.

We propose a novel neural networks model based on LSTM which is used to solve the task of classifying inertial sensor data attached to a fence with the goal of detecting security relevant incidents. To evaluate it we deployed an experimental fence surveillance system. By comparing experimental data of different approaches we find out that the neural network outperforms the baseline approach.

Along with the sophistication of society, the requirements for infrastructure systems are also becoming more sophisticated. Conventionally, infrastructure systems have been accepted if they were safe and stable, but nowadays they are required for serviceability as a matter of course. For this reason, not only the expansion of the scope of the control system but also the integration with the information service system has been frequently carried out. In this paper, we describe safety technology based on autonomous decentralized technology as one of the measures to secure safety in a control system integrating such information service functions. And we propose its future studies.

An improved radiometric calibration algorithm by extending the Mitsunaga and Nayar least-square minimization based algorithm with two major ideas is presented. First, a noise & outlier removal procedure based on the analysis of brightness transfer function is included for improving the algorithm's capability on handling noise and outlier in least-square estimation. Second, an alternative minimization formulation based on weighted least square is proposed to improve the weakness of least square minimization when dealing with biased distribution observations. The performance of the proposed algorithm with regards to two baseline algorithms is demonstrated, i.e. the classical least square based algorithm proposed by Mitsunaga and Nayar and the state-of-the-art rank minimization based algorithm proposed by Lee et al. From the results, the proposed algorithm outperforms both baseline algorithms on both the synthetic dataset and the dataset of real-world images.

To learn the working situation of rod-pumped wells under ground, we always need to analyze dynamometer diagrams, which are generated by the load sensor and displacement sensor. Rod-pumped wells are usually located in the places with extreme weather, and these sensors are installed on some special oil equipments in the open air. As time goes by, sensors are prone to generating unstable and incorrect data. Unfortunately, load sensors are too expensive to frequently reinstall. Therefore, the resulting dynamometer diagrams sometimes cannot make an accurate diagnosis. Instead, as an absolutely necessary equipment of the rod-pumped well, the electric motor has much longer life and cannot be easily impacted by the weather. The electric power curve during a swabbing period can also reflect the working situation under ground, but is much harder to explain than the dynamometer diagram. This letter presented a novel deep learning architecture, which can transform the electric power curve into the dimensionless dynamometer diagram image. We conduct our experiments on a real-world dataset, and the results show that our method can get an impressive transformation accuracy.

A design-for-testability method and an electrical interconnect test method are proposed to detect open defects occurring at interconnects among dies and input/output pins in 3D stacked ICs. As part of the design method, an nMOS and a diode are added to each input interconnect. The test method is based on measuring the quiescent current that is made to flow through an interconnect to be tested. The testability is examined both by SPICE simulation and by experimentation. The test method enabled the detection of open defects occurring at the newly designed interconnects of dies at experiments test speed of 1MHz. The simulation results reveal that an open defect generating additional delay of 279psec is detectable by the test method at a test speed of 200MHz beside of open defects that generate no logical errors.

Sparse representation has been widely applied to visual tracking for several years. In the sparse representation framework, tracking problem is transferred into solving an L1 minimization issue. However, during the tracking procedure, the appearance of target was affected by external environment. Therefore, we proposed a robust tracking algorithm based on the traditional sparse representation jointly particle filter framework. First, we obtained the observation image set from particle filter. Furthermore, we introduced a 2D transformation on the observation image set, which enables the tracking target candidates set more robust to handle misalignment problem in complex scene. Moreover, we adopt the occlusion detection mechanism before template updating, reducing the drift problem effectively. Experimental evaluations on five public challenging sequences, which exhibit occlusions, illuminating variations, scale changes, motion blur, and our tracker demonstrate accuracy and robustness in comparisons with the state-of-the-arts.

In this study, the electromagnetic scatterings from conducting bodies have been investigated via a surface equivalence theorem. When one formulates equivalent electric and magnetic currents from geometrical optics (GO) reflected field in the illuminated surface and GO incident field in the shadowed surface, it has been found that the asymptotically derived radiation fields are found to be the same as those formulated from physical optics (PO) approximation.

RTA (Response time analysis) is a popular technique to guarantee timing requirements for a real-time system, and therefore the RTA framework has been widely studied for popular scheduling algorithms such as EDF (Earliest Deadline First) and FP (Fixed Priority). While a number of extended techniques of RTA have been introduced, some of them cannot be used since they have not been proved and evaluated in terms of their correctness and empirical performance. In this letter, we address the state of the art technique of slack reclamation of the existing generic RTA framework for multiprocessors. We present its mathematical proof of correctness and empirical performance evaluation, which have not been revealed to this day.

In this study, a theory for estimating the dielectric properties for unknown materials from three reference materials without using a short condition was developed. Specifically, the relationships linking the S parameter, electrostatic capacity, the measurement instrument and the jig were determined for four equivalent circuits with three reference materials and an unknown material inserted into the jig. An equation for estimation of complex permittivity from three reference materials without short termination was thus derived. The formula's accuracy was then numerically verified for cases in which values indicating the dielectric properties of the reference materials and the actual material differed significantly, thereby verifying the effectiveness of the proposed method. Next, it was also found that dielectric constant could be correctly determined even when the observation plane was moved to the SOL calibration plane on the generator side. The dielectric properties of various liquids in the 0.50, 1.0 and 2.5 GHz bands as measured using the proposed method were then compared with corresponding conventional-method values. Finally, the validity of the proposed method was also indicated by measurement values showing the frequency characteristics of dielectric properties at frequencies ranging from 0.50 to 3.0 GHz.

With the evolution of autonomous distributed systems such as smart cities, autonomous vehicles, smart control and scheduling systems there is an increased need for approaches to manage the execution of services to deliver real-time performance. As Cloud-hosted services are increasingly used to provide intelligence and analytic functionality to Internet of Things (IoT) systems, Quality of Service (QoS) techniques must be used to guarantee the timely service delivery. This paper reviews state-of-the-art QoS and Cloud techniques for real-time service delivery and data analysis. A review of straggler mitigation and a classification of real-time QoS techniques is provided. Then a mathematical framework is presented capturing the relationship between the host execution environment and the executing service allowing the response-times to predicted throughout execution. The framework is shown experimentally to reduce the number of QoS violations by 21% and provides alerts during the first 14ms provide alerts for 94% of future violations.

A single-polarization single-mode (SPSM) photonic crystal fiber (PCF) based on double-hole unit core is proposed in this paper for application to cross-talk free polarization splitter (PS). Birefringence of the PCF is obtained by adopting double-hole unit cells into the core to destroy its symmetry. With an appropriate cladding hole size, single x- or y-polarized PCF can be achieved by arranging the double-hole unit in the core along the x- or y-axis, respectively. Moreover, our proposed SPSM PCF has the potential to be applied to consist a cross-talk free PS. The simulation result by employing a vectorial finite element beam propagation method (FE-BPM) demonstrates that an arbitrary polarized incident light can be completely separated into two orthogonal single-polarized components through the PS. The structural tolerance and wavelength dependence of the PS have also been discussed in detail.

In this study, we developed a new orthogonal partition concept for asymmetric orthogonal arrays and used it for the construction of orthogonal arrays for the first time. Permutation matrices and the Kronecker product were also successfully and skillfully used as our main tools. Hence, a new general iterative construction method for asymmetric orthogonal arrays of high strength was developed, and some new infinite families of orthogonal arrays of strength 3 and several new orthogonal arrays of strength 4, 5, and 6 were obtained.

A triangular decomposition-based multipath super-resolution method is proposed to improve the range resolution of small unmanned aerial vehicle (UAV) radar altimeters that use a single channel with continuous direct spread waveform. In the engineering applications of small UAV radar altimeter, multipath scenarios are quite common. When the conventional matched filtering process is used under these environments, it is difficult to identify multiple targets in the same range cell due to the overlap between echoes. To improve the performance, we decompose the overlapped peaks yielded by matched filtering into a series of basic triangular waveforms to identify various targets with different time-shifted correlations of the pseudo-noise (PN) sequence. Shifting the time scale enables targets in the same range resolution unit to be identified. Both theoretical analysis and experiments show that the range resolution can be improved significantly, as it outperforms traditional matched filtering processes.

To completely utilize the advantages of dynamic voltage and frequency scaling (DVFS) techniques, a quantized decoder (QNT-D) was developed. The QNT-D generates a quantized signal processing quantity (Q) using a predicted signal processing quantity (M). Q is used to produce the optimum frequency (opt.fc) and the optimum supply voltage (opt.VD) that are proportional to Q. To develop a DVFS controlled motion estimation (ME) processor, we used both the QNT-D and a fast ME algorithm called A2BC (Adaptively Assigned Breaking-off Condition) to predict M for each macro-block (MB). A DVFS controlled ME processor was fabricated using 90-nm CMOS technology. The total power dissipation (PT) of the processor was significantly reduced and varied from 38.65 to 99.5 µW, only 3.27 to 8.41 % of PT of a conventional ME processor, depending on the test video picture.

This paper presents a theoretical design of novel THz bandpass filters composed of M-PhC (metallic-photonic-crystal) point-defect-cavities (PDCs) with a centrally-loaded-rod. After a brief review of the properties of the recently-proposed M-PhC PDCs, two inline-type bandpass filters are synthesized in terms of the coupling matrix theory. The FDTD simulation results of the synthesized filters are in good agreement with the theoretical ones, which confirms the validity of the proposed filters' structures and the design scheme.

This letter describes a method that characterizes and improves the performance of a time-interleaved (TI) digital-to-analog converter (DAC) system by using multiport signal-flow graphs at microwave frequencies. A commercial signal generator with two TI DACs was characterized through s-parameter measurements and was compared to the conventional method. Moreover, prefilters were applied to correct the response, resulting in an error-vector magnitude improvement of greater than 8 dB for a 64-quadrature-amplitude-modulated signal of 4.8 Gbps. As a result, the bandwidth limitation and the complex post processing of the conventional method could be minimized.

In recent years, deep learning based image recognition, speech recognition, text translation and other related applications have brought great convenience to people's lives. With the advent of the era of internet of everything, how to run a computationally intensive deep learning algorithm on a limited resources edge device is a major challenge. For an edge oriented computing vector processor, combined with a specific neural network model, a new data layout method for putting the input feature maps in DDR, rearrangement of the convolutional kernel parameters in the nuclear memory bank is proposed. Aiming at the difficulty of parallelism of two-dimensional matrix convolution, a method of parallelizing the matrix convolution calculation in the third dimension is proposed, by setting the vector register with zero as the initial value of the max pooling to fuse the rectified linear unit (ReLU) activation function and pooling operations to reduce the repeated access to intermediate data. On the basis of single core implementation, a multi-core implementation scheme of Inception structure is proposed. Finally, based on the proposed vectorization method, we realize five kinds of neural network models, namely, AlexNet, VGG16, VGG19, GoogLeNet, ResNet18, and performance statistics and analysis based on CPU, gtx1080TI and FT2000 are presented. Experimental results show that the vector processor has better computing advantages than CPU and GPU, and can calculate large-scale neural network model in real time.

During the execution of software, tremendous amounts of data can be recorded. By exploiting the execution data, one can discover behavioral models to describe the actual software execution. As a well-known open-source process mining toolkit, ProM integrates quantities of process mining techniques and enjoys a variety of applications in a broad range of areas. How to develop a better ProM software, both from user experience and software performance perspective, are of vital importance. To achieve this goal, we need to investigate the real execution behavior of ProM which can provide useful insights on its usage and how it responds to user operations. This paper aims to propose an effective approach to solve this problem. To this end, we first instrument existing ProM framework to capture execution logs without changing its architecture. Then a two-layered framework is introduced to support accurate ProM behavior discovery by characterizing both user interaction behavior and plug-in calling behavior separately. Next, detailed discovery techniques to obtain user interaction behavior model and plug-in calling behavior model are proposed. All proposed approaches have been implemented.

In this paper, a multilevel thresholding color image segmentation method is proposed using a modified Artificial Bee Colony(ABC) algorithm. In this work, in order to improve the local search ability of ABC algorithm, Krill Herd algorithm is incorporated into its onlooker bees phase. The proposed algorithm is named as Krill herd-inspired modified Artificial Bee Colony algorithm (KABC algorithm). Experiment results verify the robustness of KABC algorithm, as well as its improvement in optimizing accuracy and convergence speed. In this work, KABC algorithm is used to solve the problem of multilevel thresholding for color image segmentation. To deal with luminance variation, rather than using gray scale histogram, a HSV space-based pre-processing method is proposed to obtain 1D feature vector. KABC algorithm is then applied to find thresholds of the feature vector. At last, an additional local search around the quasi-optimal solutions is employed to improve segmentation accuracy. In this stage, we use a modified objective function which combines Structural Similarity Index Matrix (SSIM) with Kapur's entropy. The pre-processing method, the global optimization with KABC algorithm and the local optimization stage form the whole color image segmentation method. Experiment results show enhance in accuracy of segmentation with the proposed method.