The outage performance of a multiuser two-way amplify-and-forward (AF) relaying network, where N-th best selection scheme with the consideration to the feedback delay, is investigated. Specifically, the new closed-form expressions for cumulative distribution function (CDF) and outage probability (OP) are presented over time varying Rayleigh-fading channels. Furthermore, simple approximate OP is derived assessing the high signal-to-noise-ratio (SNR), which identifies the diversity behavior. Numerical results show excellent agreement with theoretical results.

This paper describes a robust three-dimensional (3D) surface reconstruction method that can automatically eliminate shadowing errors. For modeling shadowing effect, a new shadowing compensation model based on the angle distribution of backscattered electrons is introduced. Further, it is modified with respect to some practical factors. Moreover, the proposed iterative shadowing compensation method, which performs commutatively between the compensation of image intensities and the modification of the corresponding 3D surface, can effectively provide both an accurate 3D surface and compensated shadowless images after convergence.

Automated or semi-automated annotation is a practical solution for large-scale corpus construction. However, the special characteristics of Thai language, such as lack of word-boundary and sentence-boundary markers, trigger several issues in automatic corpus annotation. This paper presents a multi-stage annotation framework, containing two stages of chunking and three stages of tagging. The two chunking stages are pattern matching-based named entity (NE) extraction and dictionary-based word segmentation while the three succeeding tagging stages are dictionary-, pattern- and statist09812490981249ical-based tagging. Applying heuristics of ambiguity priority, NE extraction is performed first on an original text using a set of patterns, in the order of pattern ambiguity. Next, the remaining text is segmented into words with a dictionary. The obtained chunks are then tagged with types of named entities or parts-of-speech (PoS) using dictionaries, patterns and statistics. Focusing on the reduction of human intervention in corpus construction, our experimental results show that the dictionary-based tagging process can assign unique tags to 64.92% of the words, with the remaining of 24.14% unknown words and 10.94% ambiguously tagged words. Later, the pattern-based tagging can reduce unknown words to only 13.34% while the statistical-based tagging can solve the ambiguously tagged words to only 3.01%.

In this paper, online sparse Volterra system identification is proposed. For that purpose, the conventional adaptive projection-based algorithm with weighted l1 balls (APWL1) is revisited for nonlinear system identification, whereby the linear-in-parameters nature of Volterra systems is utilized. Compared with sparsity-aware recursive least squares (RLS) based algorithms, requiring higher computational complexity and showing faster convergence and lower steady-state error due to their long memory in time-invariant cases, the proposed approach yields better tracking capability in time-varying cases due to short-term data dependence in updating the weight. Also, when N is the number of sparse Volterra kernels and q is the number of input vectors involved to update the weight, the proposed algorithm requires O(qN) multiplication complexity and O(Nlog 2N) sorting-operation complexity. Furthermore, sparsity-aware least mean-squares and affine projection based algorithms are also tested.

As more and more software vulnerabilities are exposed, shellcode has become very popular in recent years. It is widely used by attackers to exploit vulnerabilities and then hijack program's execution. Previous solutions suffer from limitations in that: 1) Some methods based on static analysis may fail to detect the shellcode using obfuscation techniques. 2) Other methods based on dynamic analysis could impose considerable performance overhead. In this paper, we propose Lemo, an efficient shellcode detection system. Our system is compatible with commodity hardware and operating systems, which enables deployment. To improve the performance of our system, we make use of the multi-core technology. The experiments show that our system can detect shellcode efficiently.

A directional lapped orthogonal transform (DirLOT) is an orthonormal transform of which basis is allowed to be anisotropic with the symmetric, real-valued and compact-support property. Due to its directional property, DirLOT is superior to the existing separable transforms such as DCT and DWT in expressing diagonal edges and textures. The goal of this paper is to enhance the ability of DirLOT further. To achieve this goal, we propose a novel image restoration technique using multiple DirLOTs. This paper generalizes an image denoising technique in [1], and expands the application of multiple DirLOTs by introducing linear degradation operator P. The idea is to use multiple DirLOTs to construct a redundant dictionary. More precisely, the redundant dictionary is constructed as a union of symmetric orthonormal discrete wavelet transforms generated by DirLOTs. To select atoms fitting a target image from the dictionary, we formulate an image restoration problem as an l1-regularized least square problem, which can efficiently be solved by the iterative-shrinkage/thresholding algorithm (ISTA). The proposed technique is beneficial in expressing multiple directions of edges/textures. Simulation results show that the proposed technique significantly outperforms the non-subsampled Haar wavelet transform for deblurring, super-resolution, and inpainting.

A microwave mammography setup for clinical testing was developed and used to successfully carry out an initial clinical test. The equipment is based on multistatic ultra wideband (UWB) radar, which features a multistatic microwave imaging via space time (MS-MIST) algorithm for high resolution and a conformal array with an aspirator for fixing the breast in place. In this paper, an outline of the equipment, a numerical simulation, and clinical test results are presented.

In this paper, a two-layer Multiple Kernel Learning (MKL) scheme for speaker-independent speech emotion recognition is presented. In the first layer, MKL is used for feature selection. The training samples are separated into n groups according to some rules. All groups are used for feature selection to obtain n sparse feature subsets. The intersection and the union of all feature subsets are the result of our feature selection methods. In the second layer, MKL is used again for speech emotion classification with the selected features. In order to evaluate the effectiveness of our proposed two-layer MKL scheme, we compare it with state-of-the-art results. It is shown that our scheme results in large gain in performance. Furthermore, another experiment is carried out to compare our feature selection method with other popular ones. And the result proves the effectiveness of our feature selection method.

This paper proposes an opportunistic feedback and user selection method for a multiuser two-way relay channel (MU-TWRC) in a time-varying environments where a base station (BS) and a selected mobile station (MS), one of K moving MSs, exchange messages during two time slots via an amplify-and-forward relay station. Specifically, under the assumption of perfect channel reciprocity, we analyze the outage probabilities of several channel feedback scenarios, including the proposed scheme. Based on the analysis, the transmission rates are optimized and the optimal user selection method is proposed to maximize the expected sum throughput. The simulation results indicate that, with opportunistic feedback, the performance can be significantly improved compared to that without feedback. Moreover, the performance is nearly identical to that with full feedback, and close to the case of perfect channel state information at BS for low mobility MSs.

We evaluated the behavior of a multi-user multiple-input multiple-output (MIMO) system in time-varying channels using measured data. A base station for downlink or broadcast transmission requires downlink channel state information (CSI), which is outdated in time-varying environments and we encounter degraded performance due to interference. One of the countermeasures against time-variant environments is predicting channels with an autoregressive (AR) model-based method. We modified the AR prediction for a time division duplex system. We conducted measurement campaigns in indoor environments to verify the performance of the scheme of channel prediction in an actual environment and measured channel data. We obtained the bit-error rate (BER) using these data. The AR-model-based technique of prediction assuming the Jakes' model was found to reduce BER. Also, the optimum AR-model order was investigated by using the channel data we measured.

A two-dimensional negative refractive index material is proposed. The material has a bulky structure composed of dielectric prism cells with metal patterns. The material is expressed by an equivalent circuit. The propagation regions of two left-handed modes calculated from the equivalent circuit exist near the propagation regions obtained by electromagnetic simulation. It is confirmed by simulation that the incident plane wave goes into the material with low reflection by using the second left-handed mode and attaching metal conversion strips around the material. A negative refractive index slab lens with 15×9 cells is made to measure the field distribution of wave out of the lens. It is shown that the resolution of the slab lens exceeds the diffraction-limit.

This paper presents a Complex-Valued Neural Network-based sound localization method. The proposed approach uses two microphones to localize sound sources in the whole horizontal plane. The method uses time delay and amplitude difference to generate a set of features which are then classified by a Complex-Valued Multi-Layer Perceptron. The advantage of using complex values is that the amplitude information can naturally masks the phase information. The proposed method is analyzed experimentally with regard to the spectral characteristics of the target sounds and its tolerance to noise. The obtained results emphasize and confirm the advantages of using Complex-Valued Neural Networks for the sound localization problem in comparison to the traditional Real-Valued Neural Network model.

In this letter, a fast transmit antenna selection algorithm is proposed for the spatial-temporal combining-based spatial multiplexing ultra-wideband systems on a log-normal multipath fading channel. The presented suboptimum algorithm selects the transmit antennas associated with the largest signal to noise ratio value computed by one QR decomposition operation of the full channel matrix spatially and temporally combined. It performs the iterative channel scaling operation about the channel matrix and singular value decomposition about the channel scaled matrix. It is shown that the proposed antenna selection algorithm leads to a substantial improvement in the error performance while keeping low-complexity, and obtains almost the same error performance as the exhaustive search-based optimal antenna selection algorithm.

Several broad bandwidth, electrically small, non-Foster element-augmented antennas have been designed, analyzed and measured. Both electric loop (protractor) and electric dipole (Egyptian axe) structures have been selected as the near-field resonant parasitic (NFRP) elements for these antenna designs. In order to increase their instantaneous 10dB bandwidth, negative impedance convertor (NIC)-based capacitor and inductor elements have been designed accordingly to be incorporated internally into those NFRP elements. Proper design and analysis procedures for these systems are introduced. The simulated performance characteristics of the resulting non-Foster element-augmented protractor and Egyptian axe dipole antennas are presented. Favorable comparisons with their experimentally measured values are demonstrated.

Serial RapidIO (SRIO) is a high-performance interconnection standard for embedded systems. Cyclic Redundancy Check (CRC) provides protection for packet transmissions and impacts the device performances. In this paper, two CRC calculation strategies, based on adjustable slicing parallelization and simplified calculators, are proposed. In the first scheme, the temporary CRC result of the previous cycle (CPre) is considered as a dependent input for the new cycle and is combined with a specific segment of packet data before slicing parallelization. In the second scheme, which can reach a higher maximum working frequency, CPre is considered as an independent input and is separated from the calculation of packet data for further parallelization. Performance comparisons based on ASIC and FPGA implementations are demonstrated to show their effectiveness. Compared with the reference designs, more than 34.8% and 13.9% of average power can be improved by the two proposed schemes at 156.25MHz in 130nm technology, respectively.

This paper presents a method of synthesizing covariance matrix elements of array input signal for high resolution 2-D Direction-Of-Arrival (DOA) estimation via antenna (sensor) switching. Antenna array generally has the same number of array elements and receiver modules which often leads large receiver hardware cost. Two of the authors have already studied a way of antenna switching to reduce receiver cost, but it can be applied only for periodic incident signals like sinusoid. In this paper, we propose two simple methods of DOA estimation from sparse data by synthesizing covariance matrix elements of array input signal via antenna switching, which can also be applied to DOA estimation of antiperiodic incident signals. Performance of the proposed approach is evaluated in detail through some computer simulation.

This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, estimates the direction of arrival of each source, and performs permutation alignment. We confirmed experimentally that reasonably good separation was obtained with the present method without specifying the number of sources.

Many fatal accidents involving safety-critical reactive systems have occurred in unexpected situations, which were not considered during the design and test phases of system development. To prevent such accidents, reactive systems should be designed to respond appropriately to any request from an environment at any time. Verifying this property during the specification phase reduces the development costs of safety-critical reactive systems. This property of a specification is commonly known as realizability. The complexity of the realizability problem is 2EXPTIME-complete. We have introduced the concept of strong satisfiability, which is a necessary condition for realizability. Many practical unrealizable specifications are also strongly unsatisfiable. In this paper, we show that the complexity of the strong satisfiability problem is EXPSPACE-complete. This means that strong satisfiability offers the advantage of lower complexity for analysis, compared to realizability. Moreover, we show that the strong satisfiability problem remains EXPSPACE-complete even when only formulae with a temporal depth of at most 2 are allowed.

Microwaves have typically been used for communications and radar, but nowadays are given much attention to energy transfer applications. This paper describes microwave power transfer from a satellite to Earth that is visualized as a solar power satellite system (SPSS). After the system configuration is explained, unique engineering features are presented. Then, some contributions made by Japanese community are introduced, focusing on microwave and antenna engineering. As SPSS will handle high power levels at microwave frequency, and so components should be mass-produced to reduce the cost, then we need to shift our paradigm on the technology involved. Finally, the roadmap to a commercial SPSS is discussed.

This paper reviews two simple numerical algorithms particularly useful in Computational ElectroMagnetics (CEM): the Weighted Averages (WA) algorithm and the Double Exponential (DE) quadrature. After a short historical introduction and an elementary description of the mathematical procedures underlying both techniques, they are applied to the evaluation of Sommerfeld integrals, where WA and DE combine together to provide a numerical tool of unprecedented quality. It is also shown that both algorithms have a much wider range of applications. A generalization of the WA algorithm, able to cope with integrands including products of Bessel and similar oscillatory functions, is described. Similarly, the original DE algorithm is adapted with exceptional results to the evaluation of the multidimensional singular integrals arising in the discretization of Integral-Equation based CEM formulations. The new possibilities of WA and DE algorithms are demonstrated through several practical numerical examples.