Recent theoretical and experimental studies indicate that spatial multiplexing (SM) systems have enormous potential for increasing the capacity of corresponding multiple input multiple output (MIMO) channels in rich scattering environments. In this paper, we propose a new recursion based algorithm for Bell Labs layered space time (BLAST) signal detection in SM systems. The new algorithm uses an inflated recursion in the initialization and a deflated recursion in the iteration stage: as a result, the complexity is greatly reduced and the irregularity issues are completely avoided. Compared with the conventional fastest recursive approach, the complexity of our proposal is lower by a factor of 2 and it is also very implementation friendly.

The class of complete complementary sequences (of fixed length) have the ideal correlation properties and are good at increasing the channel usage efficiency but lacks in desirable sequence lengths. In spread spectrum communication systems, sequences having nice correlation properties are important in many ways such as in suppressing multi-user interference, for reliable initial synchronization and in separation of the multipath components. It would be even good if the sequences are easy to construct and have desirable lengths for the system under consideration. In this paper, M sets of sequences that constitute a complete complementary sequences with ith set containing N sequences of length Li each, i = 0, 1, ..., M - 1, is defined and a general method that constructs such a class of complete complementary sequences (of different lengths) is given. The proposed class of complete complementary sequences, constituted by sequence sets of different lengths, does not increase the data rates when short-length sequences are employed.

This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Particle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extraction. A formulation utilizing two swarms of particles was developed to optimize simultaneously a desired performance criterion and the number of selected features. Candidate feature sets were evaluated on a regression problem. Artificial neural networks were trained to construct linear and nonlinear models of chemical concentration of glucose in soybean crops. Experimental results utilizing real-world hyperspectral datasets demonstrate the viability of the method. The particle swarms-based approach presented superior performance in comparison with conventional feature extraction methods, on both linear and nonlinear models.

The design of the finite impulse response (FIR) notch filter with controlled null width is expressed as a derivatively contrained quadratic optimization problem. The problem is transformed into an unconstrained one by choosing a null matrix orthogonal to the derivative constraint matrix. In this paper, subband decomposition using wavelet filters is employed to construct the null matrix. Taking advantage of the vanishing moment property of the wavelet filters, the proposed method can adjust the null width of the notch filter for eliminating the intractable iterference by controlling the regularity of the wavelet filters. Simulation results show that the new method can offer comparable performance as those of the existing full-rank-based ones and thus provides a promising alternative to the existing works.

This paper proposes a suboptimal algorithm for the maximum likelihood detection (MLD) in multiple-input multiple-output (MIMO) communications. The proposed algorithm regards transmitted signals as continuous variables in the same way as a common method for the discrete optimization problem, and then searches for candidates of the transmitted signals in the direction of a modified gradient vector of the metric. The vector is almost proportional to the direction of the noise enhancement, from which zero-forcing (ZF) or minimum mean square error (MMSE) algorithms suffer. This method sets the initial guess to the solution by ZF or MMSE algorithms, which can be recursively calculated. Also, the proposed algorithm has the same complexity order as that of conventional suboptimal algorithms. Computer simulations demonstrate that it is much superior in BER performance to the conventional ones.

In arithmetic circuits for digital signal processing, radixes other than two are often used to make circuits faster. In such cases, radix converters are necessary. However, in general, radix converters tend to be complex. This paper considers design methods for p-nary to binary converters. First, it considers Look-Up Table (LUT) cascade realizations. Then, it introduces a new design technique called arithmetic decomposition by using LUTs and adders. Finally, it compares the amount of hardware and performance of radix converters implemented by FPGAs. 12-digit ternary to binary converters on Cyclone II FPGAs designed by the proposed method are faster than ones by conventional methods.

In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.

This paper presents a method for blind identification of a system whose transfer matrix is non-invertible at infinity, based on independent component analysis. In the proposed scheme, the transfer matrix to be identified is pre-multiplied by an appropriate polynomial matrix, named interactor, in order to compensate the row relative degrees and obtain a biproper system. It is then pre-multiplied by a demixing matrix via an existing approximate method. Both of these matrices are estimated blindly, i.e. with the input signals being unknown. The identified system is thus obtained as the inverse of the multiplication of these matrices.

We measured the complex permittivities of whole blood and blood plasma in quasi millimeter and millimeter wave bands using a coaxial probe method. The validity of these measurements was confirmed by comparing with those of a different measurement method, i.e., a dielectric tube method. It is shown that the complex permittivities of the blood samples are similar to those of water in quasi millimeter and millimeter wave bands. Furthermore, the temperature dependences of the complex permittivities of the samples were measured.

The purpose of our study is to develop an algorithm that would enable the automated detection of lacunar infarct on T1- and T2-weighted magnetic resonance (MR) images. Automated identification of the lacunar infarct regions is not only useful in assisting radiologists to detect lacunar infarcts as a computer-aided detection (CAD) system but is also beneficial in preventing the occurrence of cerebral apoplexy in high-risk patients. The lacunar infarct regions are classified into the following two types for detection: "isolated lacunar infarct regions" and "lacunar infarct regions adjacent to hyperintensive structures." The detection of isolated lacunar infarct regions was based on the multiple-phase binarization (MPB) method. Moreover, to detect lacunar infarct regions adjacent to hyperintensive structures, we used a morphological opening processing and a subtraction technique between images produced using two types of circular structuring elements. Thereafter, candidate regions were selected based on three features -- area, circularity, and gravity center. Two methods were applied to the detected candidates for eliminating false positives (FPs). The first method involved eliminating FPs that occurred along the periphery of the brain using the region-growing technique. The second method, the multi-circular regions difference method (MCRDM), was based on the comparison between the mean pixel values in a series of double circles on a T1-weighted image. A training dataset comprising 20 lacunar infarct cases was used to adjust the parameters. In addition, 673 MR images from 80 cases were used for testing the performance of our method; the sensitivity and specificity were 90.1% and 30.0% with 1.7 FPs per image, respectively. The results indicated that our CAD system for the automatic detection of lacunar infarct on MR images was effective.

For a digitally controlled phase-locked loop (PLL), we evaluate the use of a clock-period comparator (CPC). In this PLL, only the frequency lock operation should be performed; however, the phase lock operation is also simultaneously achieved by performing the clock-period comparison when the phases of the reference signal and the output signal approach each other. Theoretically a lock-up operation was conducted. In addition, we succeeded in digitizing a voltage controlled oscillator (VCO) with a linear characteristic. We confirmed a phase lock operation with a slight loop characteristic through SPICE simulation.

With advances in ubiquitous environments, user demand for easy data-lookup is growing rapidly. Not only users but intelligent ubiquitous applications also require data-lookup services for a ubiquitous computing framework. This paper proposes a backward-compatible, searchable virtual file system (S-VFS) for easy data-lookup. We add search functionality to the VFS, the de facto standard abstraction layer over the file system. Users can find a file by its attributes without remembering the full path. S-VFS maintains the attributes and the indexing structures in a normal file per partition. It processes queries and returns the results in a form of a virtual directory. S-VFS is the modified VFS, but uses legacy file systems without any modification. Since S-VFS supports full backward compatibility, users can even browse hierarchically with the legacy path name. We implement S-VFS in Linux kernel 2.6.7-21. Experiments with randomly generated queries demonstrate outstanding lookup performance with a small overhead for indexing.

We have designed, fabricated and measured a second-order multibit switched-capacitor complex bandpass ΔΣAD modulator to evaluate our new algorithms and architecture. We propose a new structure of a complex bandpass filter in the forward path with I, Q dynamic matching, that is equivalent to the conventional one but can be divided into two separate parts. As a result, the ΔΣ modulator, which employs our proposed complex filter can also be divided into two separate parts, and there are no signal lines crossing between the upper and lower paths formed by complex filters and feedback DACs. Therefore, the layout design of the modulator can be simplified. The two sets of signal paths and circuits in the modulator are changed between I and Q while CLK is changed between high and low by adding multiplexers. Symmetric circuits are used for I and Q paths at a certain period of time, and they are switched by multiplexers to those used for Q and I paths at another period of time. In this manner, the effect of mismatches between I and Q paths is reduced. Two nine-level quantizers and four DACs are used in the modulator for low-power implementations and higher signal-to-noise-and-distortion (SNDR), but the nonlinearities of DACs are not noise-shaped and the SNDR of the ΔΣAD modulator degrades. We have also employed a new complex bandpass data-weighted averaging (DWA) algorithm to suppress nonlinearity effects of multibit DACs in complex form to achieve high accuracy; it can be realized by just adding simple digital circuitry. To evaluate these algorithms and architecture, we have implemented a modulator using 0.18 µm CMOS technology for operation at 2.8 V power supply; it achieves a measured peak SNDR of 64.5 dB at 20 MS/s with a signal bandwidth of 78 kHz while dissipating 28.4 mW and occupying a chip area of 1.82 mm2. These experimental results demonstrate the effectiveness of the above two algorithms, and the algorithms may be extended to other complex bandpass ΔΣAD modulators for application to low-IF receivers in wireless communication systems.

In order to improve the antenna gain, a composite right/left-handed (CRLH) leaky-wave (LW) antenna composed of symmetrical unit cells with short stubs terminated by vertical vias is designed. The use of symmetrical unit cells suppresses the cross-polarization of radiation to less than 23 dB. By comparing the measured radiation characteristics to that of a conventional CRLH LW antenna without short stub in the X-band, it is shown that the presented CRLH LW antenna with 51 unit cells offers a narrower beam and the antenna gain improves 4.1, 2.2 and 3.1 dB in the backward, broadside and forward directions of radiation, respectively.

A method for searching minimum Euclidean distances of respective substreams for different modulation orders of M-ary quadrature amplitude modulation signals in multiple-input and multiple-output systems is described. A channel matrix is cyclically-sorted sequentially and QR-decomposed. Using upper triangular matrices obtained by QR decomposition, minimum Euclidean distances are searched over trellis diagrams consisting of symbol-difference lattice points by computationally efficient multiple trellis-search algorithms. The simulation results demonstrate that per-substream minimum Euclidean distances can be detected with a high correct-estimation probability by path-re-searching controls over different modulation orders.

In this paper, a method of feeding point analysis is proposed for microstrip antenna that is based on the probe current compensation (PCC) method and the overlapping-grid technique (OGT) in FDTD. Generally, in the Maxwell and Ampere's differential curl equation-based FDTD, calculated error occurs in computation of the feeding point current. By applying the PCC method, the current of the feeding point can be compensated. This paper also analyzes the proposed feeding point model with cylindrical shape. When feeding point model is analyzed by rectangular coordinate, contour path error occurs. Therefore, the OGT is proposed to solve the contour path error. In the OGT, the cylindrical coordinate is applied for modeling of feeding point. In the case of using the PCC method and the OGT, the calculated error and contour path error are reduced and improved.

The latest LSIs still lack performance in pattern matching and picture recognition. Living organisms, on the other hand, devote very little energy to processing of this type, suggesting that they operate according to a fundamentally different concept. There is a notable difference between the two types of processing: the most similar pattern is always chosen by the conventional digital pattern matching process, whereas the choice made by an organism is not always the same: both the most similar patterns and other similar patterns are also chosen stochastically. To realize processing of this latter type, we examined a calculation method for stochastically selecting memorized patterns that show greater similar to the input pattern. Specifically, by the use of a random voltage sequence, we executed stochastic calculation and examined to what extent the accuracy of the solution is improved by increasing the number of random voltage sequences. Although calculation of the Manhattan distance cannot be realized by simply applying stochastic computing, it can be done stochastically by inputting the same random voltage sequence to two modules synchronously. We also found that the accuracy of the solution is improved by increasing the number of random voltage sequences. This processor operates so efficiently that the power consumption for calculation does not increase in proportion to the number of memorized vector elements. This characteristic is equivalent to a higher accuracy being obtained by a smaller number of random voltage sequences: a very promising characteristic of a stochastic associative processor.

We study quantum entanglement by Schmidt decomposition for some typical quantum algorithms. In the Shor's exponentially fast algorithm the quantum entanglement holds almost maximal, which is a major factor that a classical computer is not adequate to simulate quantum efficient algorithms.

In this paper, we propose a useful algorithm that can be applied to reduce the response time of speech recognizers based on HMM's. In our algorithm, to reduce the response time, promising HMM states are selected by single Gaussians. In speech recognition, HMM state likelihoods are evaluated by the corresponding single Gaussians first, and then likelihoods by original full Gaussians are computed and replaced only for the HMM states having relatively large likelihoods. By doing so, we can reduce the pattern-matching time for speech recognition significantly without any noticeable loss of the recognition rate. In addition, we cluster the single Gaussians into groups by measuring the distance between Gaussians. Therefore, we can reduce the extra memory much more. In our 10,000 word Korean POI (point-of-interest) recognition task, our proposed algorithm shows 35.57% reduction of the response time in comparison with that of the baseline system at the cost of 10% degradation of the WER.

In this paper, we introduce a new decision problem associated with lattices, named the Exact Length Vector Problem (ELVP), and prove the NP-completeness of ELVP in the ∞ norm. Moreover, we define two variants of ELVP. The one is a binary variant of ELVP, named the Binary Exact Length Vector Problem (BELVP), and is shown to be NP-complete in any p norm (1 ≤ p ≤ ∞). The other is a nonnegative variant of ELVP, named the Nonnegative Exact Length Vector Problem (NELVP). NELVP is defined in the 1 norm, and is also shown to be NP-complete.