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.

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.

This paper considers a multi-user large-scale multiple-input multiple-output (MIMO) system with single cell working in full-duplex mode. Maximum ratio combining/maximum ratio transmission (MRC/MRT) is applied to maximize the output signal to noise ratio (SNR) of the receiver. Then we deduce the asymptotic uplink and downlink sum rate in full-duplex mode by using the large number theorem, also giving the comparison of traditional half-duplex and full-duplex. Besides, we analyze the influence of Doppler shift on the performance of the system. Finally, the change of the system performance on the user velocity is illustrated.

In order to verify the correctness of functional requirements, we have been developing a verification method of the correctness of functional requirements specification using the Requirements Frame model. In this paper, we propose a verification method of non-functional requirements specification in terms of time-response requirements written with a natural language. We established a verification method by extending the Requirements Frame model. We have also developed a prototype system based on the method using Java. The extended Requirements Frame model and the verification method will be illustrated with examples.

We introduce a Welch FFT segment size selection method for FFT-based wide band spectrum measurement in the context of smart spectrum access (SSA), in which statistical spectrum usage information of primary users (PUs), such as duty cycle (DC), will be exploited by secondary users (SUs). Energy detectors (EDs) based on Welch FFT can detect the presence of PU signals in a broadband environment efficiently, and DC can be estimated properly if a Welch FFT segment size is set suitably. There is a trade-off between detection performance and frequency resolution in terms of the Welch FFT segment size. The optimum segment size depends on signal-to-noise ratio (SNR) which makes practical and optimum segment size setting difficult. For this issue, we previously proposed a segment size selection method employing a relationship between noise floor (NF) estimation output and the segment size without SNR information. It can achieve accurate spectrum awareness at the expense of relatively high computational complexity since it employs exhaustive search to select a proper segment size. In this paper, we propose a segment size selection method that offers reasonable spectrum awareness performance with low computational complexity since limited search is used. Numerical evaluations show that the proposed method can match the spectrum awareness performance of the conventional method with 70% lower complexity or less.

We target the estimation of antenna patterns of distributed array antenna (DAA) systems for satellite communications. Measuring DAA patterns is very difficult because of the large antenna separations involved, more than several tens of wavelengths. Our goal is to elucidate the accuracy of the DAA pattern estimation method whose inputs are actual antenna pattern data and array factors by evaluating their similarity to actually measured DAA radiation patterns. Experiments on two Ku band parabolic antennas show that their patterns can be accurately estimated even if we change the conditions such as frequency, antenna arrangement and polarization. Evaluations reveal that the method has high estimation accuracy since its errors are better than 1dB. We conclude the method is useful for the accurate estimation of DAA patterns.

Efficiently locating nodes and allocating demand has been a significant problem for telecommunication network carriers. Most of location models focused on where to locate nodes and how to assign increasing demand with optical access networks. However, the population in industrialized countries will decline over the coming decades. Recent advance in the optical amplifier technology has enabled node integration; an excess telecommunication node is closed and integrated to another node. Node integration in low-demand areas will improve the efficiency of access networks in this approaching age of depopulation. A dynamic node integration problem (DNIP) has been developed to organize the optimal plan for node integration. The problem of the DNIP was that it cannot consider the requirements of network carriers. In actual situations, network carriers often want to specify the way each node is managed, regardless of the mathematical optimality of the solution. This paper proposes a requirement modeling language (RML) for the DNIP, with which the requirements of network carriers can be described. The described statements are used to solve the DNIP, and consequently the calculated optimal solution always satisfies the requirements. The validity of the proposed method was evaluated with computer simulations in a case study.

Synthetic aperture radar (SAR) image classification is a popular yet challenging research topic in the field of SAR image interpretation. This paper presents a new classification method based on extreme learning machine (ELM) and the superpixel-guided composite kernels (SGCK). By introducing the generalized likelihood ratio (GLR) similarity, a modified simple linear iterative clustering (SLIC) algorithm is firstly developed to generate superpixel for SAR image. Instead of using a fixed-size region, the shape-adaptive superpixel is used to exploit the spatial information, which is effective to classify the pixels in the detailed and near-edge regions. Following the framework of composite kernels, the SGCK is constructed base on the spatial information and backscatter intensity information. Finally, the SGCK is incorporated an ELM classifier. Experimental results on both simulated SAR image and real SAR image demonstrate that the proposed framework is superior to some traditional classification methods.

A logo is a symbolic presentation that is designed not only to identify a product manufacturer but also to attract the attention of shoppers. Shoe logos are a challenging subject for automatic extraction and recognition using image analysis techniques because they have characteristics that distinguish them from those of other products; that is, there is much within-class variation in the appearance of shoe logos. In this paper, we propose an automatic extraction and recognition method for shoe logos with a wide variety of appearance using a limited number of training samples. The proposed method employs maximally stable extremal regions for the initial region extraction, an iterative algorithm for region grouping, and gradient features and a support vector machine for logo recognition. The results of performance evaluation experiments using a logo dataset that consists of a wide variety of appearances show that the proposed method achieves promising performance for both logo extraction and recognition.

In this paper, a new generalized cyclotomy over Zpq is presented based on cyclotomy and Chinese remainder theorem, where p and q are different odd primes. Several new construction methods for binary sequence pairs of period pq with ideal two-level correlation are given by utilizing these generalized cyclotomic classes. All the binary sequence pairs from our constructions have both ideal out-of-phase correlation values -1 and optimum balance property.

Compressed Sensing based CMOS image sensor (CS-CIS) is a new generation of CMOS image sensor that significantly reduces the power consumption. For CS-CIS, the image quality and data volume of output are two important issues to concern. In this paper, we first proposed an algorithm to generate a series of deterministic and ternary matrices, which improves the image quality, reduces the data volume and are compatible with CS-CIS. Proposed matrices are derived from the approximate DCT and trimmed in 2D-zigzag order, thus preserving the energy compaction property as DCT does. Moreover, we proposed matrix row operations adaptive to the proposed matrix to further compress data (measurements) without any image quality loss. At last, a low-cost VLSI architecture of measurements compression with proposed matrix row operations is implemented. Experiment results show our proposed matrix significantly improve the coding efficiency by BD-PSNR increase of 4.2 dB, comparing with the random binary matrix used in the-state-of-art CS-CIS. The proposed matrix row operations for measurement compression further increases the coding efficiency by 0.24 dB BD-PSNR (4.8% BD-rate reduction). The VLSI architecture is only 4.3 K gates in area and 0.3 mW in power consumption.

Existing remote collaboration systems are not suitable for a collaboration style where distributed users touch work tools at the same time, especially in demanding use cases or in severe network situations. To cover a wider range of use cases, we propose a novel concept of a remote collaboration platform that enables the users to share currently-used work tools with a high quality A/V transmission module, while maintaining the advantages of web-based systems. It also provides functions to deal with long transmission delay using relay servers, packet transmission instability using visual feedback of audio delivery and limited bandwidth using dynamic allocation of video bitrate. We implemented the platform and conducted evaluation tests. The results show the feasibility of the proposed concept and its tolerance to network constraints, which indicates that the proposed platform can construct unprecedented collaboration systems.

Irreversible thermal paints or temperature sensitive paints are a kind of special temperature sensor which can indicate the temperature grad by judging the color change and is widely used for off-line temperature measurement during aero engine test. Unfortunately, the hot gases flow within the engine during measuring always make the paint color degraded, which means a serious saturation reduction and contrast loss of the paint colors. This phenomenon makes it more difficult to interpret the thermal paint test results. Present contrast enhancement algorithms can significantly increase the image contrast but can't protect the hue feature of the paint images effectively, which always cause color shift. In this paper, we propose a color restoration method for thermal paint image. This method utilizes the atmospheric scattering model to restore the lost contrast and saturation information, so that the hue can be protected and the temperature can be precisely interpreted based on the image.

In this paper, the problem of target detection and tracking utilizing the single frequency network (SFN) is addressed. Specifically, by exploiting the characteristics of the signal in SFN, a novel likelihood model which avoids the measurement origin uncertain problem in the point measurement model is proposed. The particle filter based track-before-detect (PF-TBD) algorithm is adopted for the proposed SFN likelihood to detect and track the possibly existed target. The advantage of using TBD algorithm is that it is suitable for the condition of low SNR, and specially, in SFN, it can avoid the data association between the measurement and the transmitters. The performance of the adopted algorithm is examined via simulations.

Precise determination of antenna phase centers is crucial to reduce the uncertainty in gain when employing the three-antenna method, particularly when the range distances are short-such as a 3-m radio anechoic chamber, where the distance between the phase centers and the open ends of an aperture antenna (the most commonly-used reference) is not negligible compared with the propagation distance. An automatic system to determine the phase centers of aperture antennas in a radio anechoic chamber is developed. In addition, the absolute gain of horn antennas is evaluated using the three-antenna method. The phase centers of X-band pyramidal horns were found to migrate up to 18mm from the open end. Uncertainties in the gain were evaluated in accordance with ISO/IEC Guide 93-3: 2008. The 95% confidence interval of the horn antenna gain was reduced from 0.57 to 0.25dB, when using the phase center location instead of the open end. The phase centers, gains, polarization, and radiation patterns of space-borne antennas are measured: low and medium-gain X-band antennas for an ultra small deep space probe employing the polarization pattern method with use of the horn antenna. The 95% confidence interval in the antenna gain decreased from 0.74 to 0.47dB.

In this paper, we describe the direct learning of an end-to-end mapping between under-/over-exposed images and well-exposed images. The mapping is represented as a deep convolutional neural network (CNN) that takes multiple-exposure images as input and outputs a high-quality image. Our CNN has a lightweight structure, yet gives state-of-the-art fusion quality. Furthermore, we know that for a given pixel, the influence of the surrounding pixels gradually increases as the distance decreases. If the only pixels considered are those in the convolution kernel neighborhood, the final result will be affected. To overcome this problem, the size of the convolution kernel is often increased. However, this also increases the complexity of the network (too many parameters) and the training time. In this paper, we present a method in which a number of sub-images of the source image are obtained using the same CNN model, providing more neighborhood information for the convolution operation. Experimental results demonstrate that the proposed method achieves better performance in terms of both objective evaluation and visual quality.

Measuring program execution time is a much-used technique for performance evaluation in computer science. Without proper care, however, timed results may vary a lot, thus making it hard to trust their validity. We propose a novel timing protocol to significantly reduce such variability by eliminating executions involving infrequent, long-running daemons.

The combination of GPS measurements with the dynamic model via a Kalman filter or an extended Kalman filter, also known as GPS based reduced dynamic orbit determination (RDOD) techniques, have been widely used for accurate and real time navigation of satellites in low earth orbit (LEO). In previous studies, the GPS measurement noise variance is empirically taken as a constant, which is not reasonable because of insufficient prior information or dynamic environment. An improper estimate of the measurement noise may lead to poor performance or even divergence of the filter. In this letter, an adaptive extended Kalman filter (AEKF)-based approach using GPS dual-frequency pseudo-range measurements is presented, where the GPS pseudo-range measurement noise variance is adaptively estimated by the Carrier to Noise Ratio (C/N0) from the tracking loop of GPS receiver. The simulation results show that the proposed AEKF approach can achieve apparent improvements of the position accuracy and almost brings no extra computational burdens for satellite borne processor.

Object detection has been a hot topic of image processing, computer vision and pattern recognition. In recent years, training a model from labeled images using machine learning technique becomes popular. However, the relationship between training samples is usually ignored by existing approaches. To address this problem, a novel approach is proposed, which trains Siamese convolutional neural network on feature pairs and finely tunes the network driven by a small amount of training samples. Since the proposed method considers not only the discriminative information between objects and background, but also the relationship between intraclass features, it outperforms the state-of-arts on real images.

The ignored side effect reflecting in the introduction of mismatching brought by contrast enhancement in representative SIFT based vein recognition model is investigated. To take advantage of contrast enhancement in increasing keypoints generation, hierarchical keypoints selection and mismatching removal strategy is designed to obtain state-of-the-art recognition result.