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.

PCANet, as one noticeable shallow network, employs the histogram representation for feature pooling. However, there are three main problems about this kind of pooling method. First, the histogram-based pooling method binarizes the feature maps and leads to inevitable discriminative information loss. Second, it is difficult to effectively combine other visual cues into a compact representation, because the simple concatenation of various visual cues leads to feature representation inefficiency. Third, the dimensionality of histogram-based output grows exponentially with the number of feature maps used. In order to overcome these problems, we propose a novel shallow network model, named as PCANet-II. Compared with the histogram-based output, the second order pooling not only provides more discriminative information by preserving both the magnitude and sign of convolutional responses, but also dramatically reduces the size of output features. Thus we combine the second order statistical pooling method with the shallow network, i.e., PCANet. Moreover, it is easy to combine other discriminative and robust cues by using the second order pooling. So we introduce the binary feature difference encoding scheme into our PCANet-II to further improve robustness. Experiments demonstrate the effectiveness and robustness of our proposed PCANet-II method.

This work is conducted to solve the current problem in the attribute-based keyword search (ABKS) scheme about how to securely and efficiently delegate the search rights to other users when the authorized user is not online. We first combine proxy re-encryption (PRE) with the ABKS technology and propose a scheme called attribute-based keyword search with proxy re-encryption (PABKS). The scheme not only realizes the functions of data search and fine-grained access control, but also supports search function sharing. In addition, we randomly blind the user's private key to the server, which ensures the confidentiality and security of the private key. Then, we also prove that the scheme is selective access structure and chosen keyword attack (IND-sAS-CKA) secured in the random oracle model. A performance analysis and security proof show that the proposed scheme can achieve efficient and secure data search in the cloud.

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.

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.

With the expansion of the Internet, the number of available Web services has increased. Web service clustering to identify functionally similar clusters has become a major approach to the efficient discovery of suitable Web services. In this study, we propose a Web service clustering approach that uses novel ontology learning and a similarity calculation method based on the specificity of an ontology in a domain with respect to information theory. Instead of using traditional methods, we generate the ontology using a novel method that considers the specificity and similarity of terms. The specificity of a term describes the amount of domain-specific information contained in that term. Although general terms contain little domain-specific information, specific terms may contain much more domain-related information. The generated ontology is used in the similarity calculations. New logic-based filters are introduced for the similarity-calculation procedure. If similarity calculations using the specified filters fail, then information-retrieval-based methods are applied to the similarity calculations. Finally, an agglomerative clustering algorithm, based on the calculated similarity values, is used for the clustering. We achieved highly efficient and accurate results with this clustering approach, as measured by improved average precision, recall, F-measure, purity and entropy values. According to the results, specificity of terms plays a major role when classifying domain information. Our novel ontology-based clustering approach outperforms comparable existing approaches that do not consider the specificity of terms.

Software-defined networking (SDN) is recognized as the next-generation networking paradigm. The software-defined architecture for underwater acoustic sensor networks (SDUASNs) has become a hot topic. However, the current researches on SDUASNs is still in its infancy, which mainly focuses on network architecture, data transmission and routing. There exists some shortcomings that the scale of the SDUASNs is difficult to expand, and the security maintenance is seldom dabble. Therefore, a scalable software-definition architecture for underwater acoustic sensor networks (SSDUASNs) is introduced in this paper. It realizes an organic combination of the knowledge level, control level, and data level. The new nodes can easily access the network, which could be conducive to large-scale deployment. Then, the basic security authentication mechanism called BSAM is designed based on our architecture. In order to reflect the advantages of flexible and programmable in SSDUASNs, security authentication mechanism with pre-push (SAM-PP) is proposed in the further. In the current UASNs, nodes authentication protocol is inefficient as high consumption and long delay. In addition, it is difficult to adapt to the dynamic environment. The two mechanisms can effectively solve these problems. Compared to some existing schemes, BSAM and SAM-PP can effectively distinguish between legal nodes and malicious nodes, save the storage space of nodes greatly, and improve the efficiency of network operation. Moreover, SAM-PP has a further advantage in reducing the authentication delay.

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.

In this paper we extend hyperparameter-free sparse signal reconstruction approaches to permit the high-resolution time delay estimation of spread spectrum signals and demonstrate their feasibility in terms of both performance and computation complexity by applying them to the ISO/IEC 24730-2.1 real-time locating system (RTLS). Numerical examples show that the sparse asymptotic minimum variance (SAMV) approach outperforms other sparse algorithms and multiple signal classification (MUSIC) regardless of the signal correlation, especially in the case where the incoming signals are closely spaced within a Rayleigh resolution limit. The performance difference among the hyperparameter-free approaches decreases significantly as the signals become more widely separated. SAMV is sometimes strongly influenced by the noise correlation, but the degrading effect of the correlated noise can be mitigated through the noise-whitening process. The computation complexity of SAMV can be feasible for practical system use by setting the power update threshold and the grid size properly, and/or via parallel implementations.

This paper investigates approaches that can cancel nonlinear phase noise effectively for the phase-conjugate pair diversity transmission of 16-QAM WDM signals through multi-core fiber. The geometric mean is introduced for the combination of the phase-conjugate pair. A numerical simulation suggests that span-by-span chromatic dispersion compensation is more effective at cancelling phase noise in long distance transmission than lumped compensation at the receiver. Simulations suggest the span-wise compensation described herein yields Q-value enhancement of 7.8 and 6.8dB for CD values of 10 and 20.6ps/nm/km, respectively, whereas the lumped compensation equivalent attains only 3.5dB. A 1050km recirculating loop experiment confirmed a Q-value enhancement of 4.1dB for 20.6ps/nm/km, span-wise compensation transmission.

This paper presents an adaptive least-significant-bit (LSB) steganography for spatial color images on smartphones. For each red, green, and blue color component, the combinations of All-4bit, One-4bit+Two-2bit, and Two-3bit+One-2bit LSB replacements are proposed for content-adaptivity and natural histograms. The high capacity of 8.4bpp with the average peak signal noise ratio (PSNR) 43.7db and fast processing times on smartphones are also demonstrated

A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.

As the capabilities and costs of Artificial Intelligence (AI) and of sensors (IoT) continue to improve, the concept of a “control system” can evolve beyond the operation of a discrete technical system based on numerical information and enter the realm of large-scale systems with both technical and social characteristics based on both numerical and unstructured information. This evolution has particular significance for applying the principles of Autonomous Decentralised Systems (ADS) [1]. This article considers the possible roles for ADS in complex technical and social systems extending up to global scales.