The applicability in harsh optical environments, such as dark smog, or strong backlight of ultra-wide band (UWB) pulse radar has a definite advantage over optical ranging techniques. We have already proposed the extended Synthetic Aperture Radar (SAR) algorithm employing double scattered waves, which aimed at enhancing the reconstructible region of the target boundary including shadow region. However, it still suffers from the shadow area for the target that has a sharp inclination or deep concave boundary, because it assumes a mono-static model, whose real aperture size is, in general, small. To resolve this issue, this study proposes an extension algorithm of the double scattered SAR based on a multi-static configuration. While this extension is quite simple, the effectiveness of the proposed method is nontrivial with regard to the expansion of the imaging range. The results from numerical simulations verify that our method significantly enhances the visible range of the target surfaces without a priori knowledge of the target shapes or any preliminary observation of its surroundings.

Complex-valued Associative Memory (CAM) is an advanced model of Hopfield Associative Memory. The CAM is based on multi-state neurons and has the high ability of representation. Lee proposed gradient descent learning for the CAM to improve the storage capacity. It is based on only the phases of input signals. In this paper, we propose another type of gradient descent learning based on both the phases and the amplitude. The proposed learning method improves the noise robustness and accelerates the learning speed.

This paper proposes a low-complexity signal detection algorithm for spatially correlated multiple-input multiple-output (MIMO) channels. The proposed algorithm sets a minimum mean-square error (MMSE) detection result to the starting point, and searches for signal candidates in multi-dimensions of the noise enhancement from which the MMSE detection suffers. The multi-dimensional search is needed because the number of dominant directions of the noise enhancement is likely to be more than one over the correlated MIMO channels. To reduce the computational complexity of the multi-dimensional search, the proposed algorithm limits the number of signal candidates to O(NT) where NT is the number of transmit antennas and O( ) is big O notation. Specifically, the signal candidates, which are unquantized, are obtained as the solution of a minimization problem under a constraint that a stream of the candidates should be equal to a constellation point. Finally, the detected signal is selected from hard decisions of both the MMSE detection result and unquantized signal candidates on the basis of the log likelihood function. For reducing the complexity of this process, the proposed algorithm decreases the number of calculations of the log likelihood functions for the quantized signal candidates. Computer simulations under a correlated MIMO channel condition demonstrate that the proposed scheme provides an excellent trade-off between BER performance and complexity, and that it is superior to conventional one-dimensional search algorithms in BER performance while requiring less complexity than the conventional algorithms.

Fourier transform is a significant tool in image processing and pattern recognition. By introducing a hypercomplex number, hypercomplex Fourier transform treats a signal as a vector field and generalizes the conventional Fourier transform. Inspired from that, hypercomplex polar Fourier analysis that extends conventional polar Fourier analysis is proposed in this paper. The proposed method can handle signals represented by hypercomplex numbers as color images. The hypercomplex polar Fourier analysis is reversible that means it can be used to reconstruct image. The hypercomplex polar Fourier descriptor has rotation invariance property that can be used for feature extraction. Due to the noncommutative property of quaternion multiplication, both left-side and right-side hypercomplex polar Fourier analysis are discussed and their relationships are also established in this paper. The experimental results on image reconstruction, rotation invariance, color plate test and image retrieval are given to illustrate the usefulness of the proposed method as an image analysis tool.

In this letter, we determine the linear complexity and minimum polynomial of the frequency hopping sequences over GF(q) introduced by Chung and Yang, where q is an odd prime. The results of this letter show that these sequences are quite good from the linear complexity viewpoint. By modifying these sequences, another class of frequency hopping sequences are obtained. The modified sequences also have low Hamming autocorrelation and large linear complexity.

This research proposes a Coding-Gain-Based (CGB) complexity control method for real-time H.264 video encoding in complexity-constrained systems such as wireless handsets. By allocating more complexity to the encoding tools which have higher coding efficiency, the CGB method is able to maximize the overall coding efficiency of the encoder.

An unsupervised algorithm is proposed for learning overcomplete topographic representations of nature image. Our method is based on Independent Component Analysis (ICA) model due to its superiority on feature extraction, and overcomes the weakness of traditional method in fast overcomplete learning. Besides, the learnt topographic representation, resembling receptive fields of complex cells, can be used as descriptors to extract invariant features. Recognition experiments on Caltech-101 dataset confirm that these complex cell descriptors are not only efficient in feature extraction but achieve comparable performances to traditional descriptors.

In this paper, a linear optimization of the dual-tree complex wavelet transform (DTCWT) based on the least squares method is proposed. The proposed method can design efficient DTCWTs by improving the design degrees of freedom and solving the least square solution iteratively. Because the resulting DTCWTs have good approximation accuracy of the half sample delay condition and the stopband attenuation, they provide precise shift-invariance and directionality. Finally, the proposed DTCWTs are evaluated by applying to non-linear approximation and image denoising, and showed their effectiveness, compared with the conventional DTCWTs.

If a d-dimensional pure simplicial complex C has a shelling, which is a specific total order of all facets of C, C is said to be shellable. We consider the problem of deciding whether C is shellable or not. This problem is solved in linear time of m, the number of all facets of C, if d = 1 or C is a pseudomanifold in d = 2. Otherwise it is unknown at this point whether the decision of shellability can be solved in polynomial time of m. Thus, for the latter case, we had no choice but to apply a brute force method to the decision problem; namely checking up to the m! ways to see if one can arrange all the m facets of C into a shelling. In this paper, we introduce a new concept, called h-assignment, to C and propose a practical method using h-assignments to decide whether C is shellable or not. Our method can make the decision of shellability of C by smaller sized computation than the brute force method.

A fifth-order switched-capacitor (SC) complex filter was implemented in 0.2-µm CMOS technology. A novel SC integrator was developed to reduce the die size and current consumption of the filter. The filter is centered at 24.730.15 kHz (3δ) and has a bandwidth of 20.260.3 kHz (3δ). The image channel is attenuated by more than 42.6 dB. The in-band third-order harmonic input intercept point (IIP3) is 17.3 dBm, and the input referred RMS noise is 34.3 µVrms. The complex filter consumes 350 µA with a 2.0-V power supply. The die size is 0.578 mm2. Owing to the new SC integrator, the filter achieves a 27% reduction in die size without any degradation in its characteristics, including its noise performance, compared with the conventional equivalent.

Let p be an odd prime number. We define a family of quaternary sequences of period 2p using generalized cyclotomic classes over the residue class ring modulo 2p. We compute exact values of the linear complexity, which are larger than half of the period. Such sequences are 'good' enough from the viewpoint of linear complexity.

In this letter a simplified Jury's table for real polynomials is extended to complex polynomials. Then it is shown that the extended table contains information on the root distribution of complex polynomials with respect to the unit circle in the complex plane. The result given in this letter is distinct from the recent one in that root counting is performed in a different way.

In the paper, a technique of the numerical inversion of multidimensional Laplace transforms (nD NILT), based on a complex Fourier series approximation is elaborated in light of a possible ralative error achievable. The detailed error analysis shows a relationship between the numerical integration of a multifold Bromwich integral and a complex Fourier series approximation, and leads to a novel formula relating the limiting relative error to the nD NILT technique parameters.

We create an efficient statistical pruning (SP) algorithm for fixed-complexity sphere decoder (FSD) by utilizing partial decision feedback detection (i.e., SP-FSD). Simulation results show that SP-FSD not only attains the near-optimal performance, but also achieves much lower complexity than the original FSD and its two lately-developed variants: the simplified FSD (SFSD) and the statistical threshold-based FSD (ST-FSD).

Fractal structures emerge from statistical and hierarchical processes in urban development or network evolution. In a class of efficient and robust geographical networks, we derive the size distribution of layered areas, and estimate the fractal dimension by using the distribution without huge computations. This method can be applied to self-similar tilings based on a stochastic process.

Some new generalized cyclotomic sequences defined by C. Ding and T. Helleseth are proven to exhibit a number of good randomness properties. In this paper, we determine the defining pairs of these sequences of length pm (p prime, m ≥ 2) with order two, then from which we obtain their trace representation. Thus their linear complexity can be derived using Key's method.

ICA (Independent Component Analysis) has a remarkable capability of separating mixtures of stochastic random signals. However, we often face problems of separating mixtures of deterministic signals, especially sinusoidal signals, in some applications such as radar systems and communication systems. One may ask if ICA is effective for deterministic signals. In this paper, we analyze the basic performance of ICA in separating mixtures of complex sinusoidal signals, which utilizes the fourth order cumulant as a criterion of independency of signals. We theoretically show that ICA can separate mixtures of deterministic sinusoidal signals. Then, we conduct computer simulations and radio experiments with a linear array antenna to confirm the theoretical result. We will show that ICA is successful in separating mixtures of sinusoidal signals with frequency difference less than FFT resolution and with DOA (Direction of Arrival) difference less than Rayleigh criterion.

This paper proposes a low-complexity algorithm to calculate log likelihood ratios (LLRs) of coded bits, which is necessary for channel decoding in coded MIMO mobile communications. An approximate LLR needs to find a pair of transmitted signal candidates that can maximize the log likelihood function under a constraint that a coded bit is equal to either one or zero. The proposed algorithm can find such a pair simultaneously, whereas conventional ones find them individually. Specifically, the proposed method searches for such candidates in directions of the noise enhancement using the MMSE detection as a starting point. First, an inverse matrix which the MMSE weight matrix includes is obtained and then the power method derives eigenvectors of the inverse matrix as the directions of the noise enhancement. With some eigenvectors, one-dimensional search and hard decision are performed. From the resultant signals, the transmitted signal candidates to be required are selected on the basis of the log likelihood function. Computer simulations with 44 MIMO-OFDM, 16QAM, and convolutional codes (rate =1/2, 2/3) demonstrate that the proposed algorithm requires only 1.0 dB more Eb/N0 than that of the maximum likelihood detection (MLD) in order to achieve packet error rate of 10-3, while reducing the complexity to about 0.2% of that of MLD.

In this letter, we generalize the binary sequence introduced by Li et al. in [S. Q. Li et al., On the randomness generalized cyclotomic sequences of order two and length pq, IEICE Trans. Fund, vol. E90-A, no.9, pp.2037-2041, 2007] to sequence over arbitrary prime fields. Furthermore, the auto-correlation distribution and linear complexity of the proposed sequence are presented.

Ubiquitous technologies such as sensor network and RFID have enabled companies to realize more rapid and agile manufacturing and service systems. In this paper, we addresses how the huge amount of real-time events coming from these devices can be filtered and integrated to business process such as manufacturing, logistics, and supply chain process. In particular, we focus on complex event processing of sensor and RFID events in order to integrate them to business rules in business activities. We also illustrate a ubiquitous event processing system, named ueFilter, which helps to filter and aggregate sensor event, to detect event patterns from sensors and RFID by means of event pattern languages (EPL), and trigger event-condition-action (ECA) in logistics processes.