Dramatic gains in channel capacity can be achieved in the closed-loop MIMO system under the assumption that the base station (BS) can acquire the downlink channel state information (CSI) accurately. However, transmitting CSI with high precision is a heavy burden that wastes a lot of uplink bandwidth, while transmitting CSI within a limited bandwidth leads to the degradation of system performance. To address this problem, we propose a zero-overhead downlink CSI feedback scheme based on the hybrid pilot structure. The downlink CSI is contained in the hybrid pilots at mobile terminal (MT) side, fed back to BS via the uplink pilot channel, and recovered from hybrid pilot at BS side. Meanwhile the uplink channel is estimated based on the hybrid pilot at BS side. Since transmitting the hybrid pilots occupies the same bandwidth as transmitting traditional code division multiplexing based uplink pilots, no extra uplink channel bandwidth is occupied. Therefore, the overhead for downlink CSI feedback is zero. Moreover, the hybrid pilots are formed at MT side by superposing the received analog downlink pilots directly on the uplink pilots. Thus the downlink CSI estimation process is unnecessary at MT side, and MT's complexity can be reduced. Numerical Simulations prove that, the proposed downlink CSI feedback has the higher precision than the traditional feedback schemes while the overhead for downlink CSI feedback is zero.

In this research a new prediction algorithm based on a Fuzzy Mix of Filters (FMF) is developed. The use of a fuzzy mix is a good solution because it makes intuitive the difficult design task of combining several types of filters, so that the outputs of the filters that work closer to their optimal behavior have higher influence in the predicted values. Therefore the FMF adapts, according to the motion of the tracked object or target, the filter weights to reduce the estimation error. The paper develops the theory about the FMF and uses it for applications with hard real-time requirements. The improvement of the proposed FMF is shown in simulation and an implementation on a parallel processor (FPGA) is presented. As a practical application of the FMF, experimental results are provided for a visual servoing task.

OFDM (Orthogonal Frequency Division Multiplexing) is widely used in wideband wireless communication systems due to its excellent performance. One of the most important operations in OFDM receivers is preamble detection. This paper addresses a general form of extended differential detection methods, which is a combination of differential detection and a moving average filter. This paper also presents a filter size determination method that achieves satisfactory performance in various channel environments.

This letter proposes a new adaptive filtering method that uses the last L desired signal samples as an extra input vector, besides the existing input data, to reduce mean square error. We have improved the convergence rate by adopting the squared norm of the past error samples, in addition to the modified cost function. The modified variable error-data normalized step-size least mean square algorithm provides fast convergence, ensuring a small final misadjustment. Simulation results indicate its superior mean square error performance, while its convergence rate equals that of existing methods. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance.

This paper presents an extended Relief-F algorithm for nominal attribute estimation, for application to small-document classification. Relief algorithms are general and successful instance-based feature-filtering algorithms for data classification and regression. Many improved Relief algorithms have been introduced as solutions to problems of redundancy and irrelevant noisy features and to the limitations of the algorithms for multiclass datasets. However, these algorithms have only rarely been applied to text classification, because the numerous features in multiclass datasets lead to great time complexity. Therefore, in considering their application to text feature filtering and classification, we presented an extended Relief-F algorithm for numerical attribute estimation (E-Relief-F) in 2007. However, we found limitations and some problems with it. Therefore, in this paper, we introduce additional problems with Relief algorithms for text feature filtering, including the negative influence of computation similarities and weights caused by a small number of features in an instance, the absence of nearest hits and misses for some instances, and great time complexity. We then suggest a new extended Relief-F algorithm for nominal attribute estimation (E-Relief-Fd) to solve these problems, and we apply it to small text-document classification. We used the algorithm in experiments to estimate feature quality for various datasets, its application to classification, and its performance in comparison with existing Relief algorithms. The experimental results show that the new E-Relief-Fd algorithm offers better performance than previous Relief algorithms, including E-Relief-F.

A novel bandpass filter (BPF) for an ultra-wideband (UWB) system is proposed in this letter. The BPF consists of four coplanar stripline (CPS)-to-microstrip transitions. Each transition is employed for broad electromagnetic (EM) coupling between a short-circuited CPS and an open-circuited microstrip line. The equivalent circuit model of the proposed geometry is derived and utilized in the impedance and mode matching analysis. Measured results show good agreement with the analysis and simulated ones.

An OFDMA based channel access scheme is proposed for dynamic spectrum access to utilize frequency spectrum efficiently. Though the OFDMA based scheme is flexible enough to change the bandwidth and channel of the transmitted signals, the OFDMA signal has large PAPR (Peak to Average Power Ratio). In addition, if the OFDMA receiver does not use a filter to extract sub-carriers before FFT (Fast Fourier Transform) processing, the designated sub-carriers suffer large interference from the adjacent channel signals in the FFT processing on the receiving side. To solve the problems such as PAPR and adjacent channel interference encountered in the OFDMA based scheme, this paper proposes a novel dynamic channel access scheme using overlap FFT filter-bank based on single carrier modulation. It also shows performance evaluation results of the proposed scheme by computer simulation.

This paper presents a prototype group modem for a hyper-multipoint data gathering satellite communication system. It can handle arbitrarily and dynamically assigned FDMA signals by employing a novel FFT-type block demultiplexer/multiplexer. We clarify its configuration and operational principle. Experiments show that the developed modem offers excellent performance.

Alamouti's orthogonal space-time block code (OSTBC) is a simple yet important technique to take advantage of transmit diversity to mitigate fading channel effects. In this paper, we analyze the effects of time-selective channels and channel estimation errors on the bit error rate (BER) performance of Alamouti's OSTBC. We develop an analytical expression of the BER performance for the linear decoding with minimum mean squared error (MMSE) channel estimates in place of the true channel. Based on the expression, we derive a BER performance limit in decision-directed mode where the channel is tracked with Kalman filtering. Numerical examples are provided to validate our analysis and to see the impact of time-selective fading and channel estimation errors on the BER performance.

Bloom filters are widely used for various network applications. Because of the limited size of on-chip memory and the large volume of network traffic, Bloom filters are often required to update their contents incrementally. Two techniques have been used for this purpose: cold cache and double buffering. Cold cache outperforms double buffering in terms of the average cache ratio. However, double buffering works much better than cold cache for the worst-case cache hit ratio. In this paper, we propose a new updating scheme for Bloom filters, which updates the contents of Bloom filter incrementally while the assigned memory space is fully utilized. The proposed scheme works better than cold cache in terms of the average cache hit ratio. At the same time, it outperforms double buffering for the worst-case cache hit ratio.

Recently, many researchers tackle accurate object recognition algorithms and many algorithms are proposed. However, these algorithms have some problems caused by variety of real environments such as a direction change of the object or its shading change. The new tracking algorithm, Cascade Particle Filter, is proposed to fill such demands in real environments by constructing the object model while tracking the objects. We have been investigating to implement accurate object recognition on embedded systems in real-time. In order to apply the Cascade Particle Filter to embedded applications such as surveillance, automotives, and robotics, a hardware accelerator is indispensable because of limitations in power consumption. In this paper we propose a hardware implementation of the Discrete AdaBoost algorithm that is the most computationally intensive part of the Cascade Particle Filter. To implement the proposed hardware, we use PICO Express, a high level synthesis tool provided by Synfora, for rapid prototyping. Implementation result shows that the synthesized hardware has 1,132,038 transistors and the die area is 2,195 µm 1,985 µm under a 0.180 µm library. The simulation result shows that total processing time is about 8.2 milliseconds at 65 MHz operation frequency.

Quantification of the hip cartilages is clinically important. In this study, we propose an automatic technique for segmentation and visualization of the acetabular and femoral head cartilages based on clinically obtained multi-slice T1-weighted MR data and a hybrid approach. We follow a knowledge based approach by employing several features such as the anatomical shapes of the hip femoral and acetabular cartilages and corresponding image intensities. We estimate the center of the femoral head by a Hough transform and then automatically select the volume of interest. We then automatically segment the hip bones by a self-adaptive vector quantization technique. Next, we localize the articular central line by a modified canny edge detector based on the first and second derivative filters along the radial lines originated from the femoral head center and anatomical constraint. We then roughly segment the acetabular and femoral head cartilages using derivative images obtained in the previous step and a top-hat filter. Final masks of the acetabular and femoral head cartilages are automatically performed by employing the rough results, the estimated articular center line and the anatomical knowledge. Next, we generate a thickness map for each cartilage in the radial direction based on a Euclidian distance. Three dimensional pelvic bones, acetabular and femoral cartilages and corresponding thicknesses are overlaid and visualized. The techniques have been implemented in C++ and MATLAB environment. We have evaluated and clarified the usefulness of the proposed techniques in the presence of 40 clinical hips multi-slice MR images.

This paper focuses on fusion estimation algorithms weighted by matrices and scalars, and relationship between them is considered. We present new algorithms that address the computation of matrix weights arising from multidimensional estimation problems. The first algorithm is based on the Cholesky factorization of a cross-covariance block-matrix. This algorithm is equivalent to the standard composite fusion estimation algorithm however it is low-complexity. The second fusion algorithm is based on an approximation scheme which uses special steady-state approximation for local cross-covariances. Such approximation is useful for computing matrix weights in real-time. Subsequent analysis of the proposed fusion algorithms is presented, in which examples demonstrate the low-computational complexity of the new fusion estimation algorithms.

This letter proposes a windowing frequency domain adaptive algorithm, which reuses the filtering error to apply window function in the filter updating symmetrically. By using a proper window function to reduce the negative influence of the spectral leakage, the proposed algorithm can significantly improve the performance of the acoustic echo cancellation for speech signals.

In this paper, an image restoration method using the Wiener filter is proposed. In order to bring the theory of the Wiener filter consistent with images that have spatially varying statistics, the proposed method adopts the locally adaptive Wiener filter (AWF) based on the universal Gaussian mixture distribution model (UNI-GMM) previously proposed for denoising. Applying the UNI-GMM-AWF for deconvolution problem, the proposed method employs the stationary Wiener filter (SWF) as a pre-filter. The SWF in the discrete cosine transform domain shrinks the blur point spread function and facilitates the modeling and filtering at the proceeding AWF. The SWF and UNI-GMM are learned using a generic training image set and the proposed method is tuned toward the image set. Simulation results are presented to demonstrate the effectiveness of the proposed method.

This paper describes the architecture and implementations of an automatic digital tuning circuit for a complex bandpass filter (BPF) in a low-power and low-cost transceiver for applications such as personal authentication and wireless sensor network systems. The architectural design analysis demonstrates that an active RC filter in a low-IF architecture can be at least 47.7% smaller in area than a conventional gm-C filter; in addition, it features a simple implementation of an associated tuning circuit. The principle of simultaneous tuning of both the center frequency and bandwidth through calibration of a capacitor array is illustrated as based on an analysis of filter characteristics, and a scalable automatic digital tuning circuit with simple analog blocks and control logic having only 835 gates is introduced. The developed capacitor tuning technique can achieve a tuning error of less than 3.5% and lower a peaking in the passband filter characteristics. An experimental complex BPF using 0.18 µm CMOS technology can successfully reduce the tuning error from an initial value of -20% to less than 2.5% after tuning. The filter block dimensions are 1.22 mm1.01 mm; and in measurement results of the developed complex BPF with the automatic digital tuning circuit, current consumption is 705 µA and the image rejection ratio is 40.3 dB. Complete evaluation of the BPF indicates that this technique can be applied to low-power, low-cost transceivers.

This paper describes the characteristic of negative group delay (NGD) circuits for various configurations including first-order, distributed, and second-order RC circuit configurations. This study includes locus, magnitude, and phase characteristics of the NGD circuits. The simplest NGD circuit is available using first-order RC or RL configuration. As an example of distributed circuit configuration, it is verified that losses in a distributed line causes NGD characteristic at higher cut-off band of a coupled four-line bandpass filter. Also, novel wideband NGD circuits using second-order RC configuration, instead of conventional RLC configuration, are proposed. Adding a parallel resistor to a parallel-T filter enables NGD characteristic to it. Also, a Wien-Robinson bridge is modified to have NGD characteristic by controlling the voltage division ratio. They are fabricated on MMIC substrate, and their NGD characteristics are verified with measured results. They have larger insertion loss than multi-stage RLC NGD circuits, however they can realize second-order NGD characteristic without practical implementation of inductors.

To obtain text information included in a scene image, we first need to extract text regions from the image before recognizing the text. In this paper, we examine human vision and propose a novel method to extract text regions by evaluating textural variation. Human beings are often attracted by textural variation in scenes, which causes foveation. We frame a hypothesis that texts also have similar property that distinguishes them from the natural background. In our method, we calculate spatial variation of texture to obtain the distribution of the degree of likelihood of text region. Here we evaluate the changes in local spatial spectrum as the textural variation. We investigate two options to evaluate the spectrum, that is, those based on one- and two-dimensional Fourier transforms. In particular, in this paper, we put emphasis on the one-dimensional transform, which functions like the Gabor filter. The proposal can be applied to a wide range of characters mainly because it employs neither templates nor heuristics concerning character size, aspect ratio, specific direction, alignment, and so on. We demonstrate that the method effectively extracts text regions contained in various general scene images. We present quantitative evaluation of the method by using databases open to the public.

A rotary inverted pendulum is stabilized by a single first order dynamic output feedback system. Numerical simulations and experimental results show that the proposed control law has low-pass filter property as well as it can successfully replace the velocity measurements for LQR control law.

Kalman filters are effective channel estimators but they have the drawback of having heavy calculations when filtering needs to be done in each sample for a large number of subcarriers. In our paper we obtain the steady-state Kalman gain to estimate the channel state by utilizing the characteristics of pilot subcarriers in OFDM, and thus a larger portion of the calculation burden can be eliminated. Steady-state value is calculated by transforming the vector Kalman filtering in to scalar domain by exploiting the filter charactertics when pilot subcarriers are used for channel estimation. Kalman filters operate optimally in the steady-state condition. Therefore by avoiding the convergence period of the Kalman gain, the proposed scheme is able to perform better than the conventional method. Also, driving noise variance of the channel is difficult to obtain practical situations and accurate knowledge is important for the proper operation of the Kalman filter. Therefore, we extend our scheme to operate in the absence of the knowledge of driving noise variance by utilizing received Signal-to-Noise Ratio (SNR). Simulation results show considerable estimator performance gain can be obtained compared to the conventional Kalman filter.