This letter presents a boundary-active-only (BAO) power reduction technique for cell-network-based region-growing video segmentation. The key approach is an adaptive situation-dependent power switching of each network cell, namely only cells at the boundary of currently grown regions are activated, and all the other cells are kept in low-power stand-by mode. The effectiveness of the proposed technique is experimentally confirmed with CMOS test-chips having small-scale cell networks of up to 4133 cells, where an average of only 1.7% of the cells remains active after application of the proposed approach. About 85% power reduction is thus achievable without sacrificing real-time processing.

We present a training algorithm to create a neural network (NN) ensemble that performs classification tasks. It employs a competitive decay of hidden nodes in the component NNs as well as a selective deletion of NNs in ensemble, thus named a pruning algorithm for NN ensembles (PNNE). A node cooperation function of hidden nodes in each NN is introduced in order to support the decaying process. The training is based on the negative correlation learning that ensures diversity among the component NNs in ensemble. The less important networks are deleted by a criterion that indicates over-fitting. The PNNE has been tested extensively on a number of standard benchmark problems in machine learning, including the Australian credit card assessment, breast cancer, circle-in-the-square, diabetes, glass identification, ionosphere, iris identification, and soybean identification problems. The results show that classification performances of NN ensemble produced by the PNNE are better than or competitive to those by the conventional constructive and fixed architecture algorithms. Furthermore, in comparison to the constructive algorithm, NN ensemble produced by the PNNE consists of a smaller number of component NNs, and they are more diverse owing to the uniform training for all component NNs.

In this paper, we introduce a new method for depth perception from a 2D natural scene using scale variation of patterns. As the surface from a 2D scene gets farther away from us, the texture appears finer and smoother. Texture gradient is one of the monocular depth cues which can be represented by gradual scale variations of textured patterns. To extract feature vectors from textured patterns, higher order local autocorrelation functions are utilized at each scale step. The hierarchical linear discriminant analysis is employed to classify the scale rate of the feature vector which can be divided into subspaces by recursively grouping the overlapped classes. In the experiment, relative depth perception of 2D natural scenes is performed on the proposed method and it is expected to play an important role in natural scene analysis.

The module-wise dynamic voltage and frequency scaling (MDVFS) scheme is applied to a single-chip H.264/MPEG-4 audio/visual codec LSI. The power consumption of the target module with controlled supply voltage and frequency is reduced by 40% in comparison with the operation without voltage or frequency scaling. The consumed power of the chip is 63 mW in decoding QVGA H.264 video at 15 fps and MPEG-4 AAC LC audio simultaneously. This LSI keep operating continuously even during the voltage transition of the target module by introducing the newly developed dynamic de-skewing system (DDS) which watches and control the clock edge of the target module.

Cooperative transmission among users in multiple access wireless environments is an efficient way to obtain the powerful benefits of multi-antenna systems without the need for physical arrays. Its performance in relay networks were extensively analyzed but most papers only focused on the case of high signal-to-noise ratio (SNR) or loose power constraints. Based on the approximation of total SNR distributions of the propagation paths through relays as the exponential functions, we derive the analytical BER expression in a simple form for general relay networks under strict power restriction. Numerical and simulation results reveal the high accuracy of the distribution estimation as well as the high reliability of the deduced formula, especially at the low SNR.

In closed-loop multiple-input and multiple-output space-division multiplexing (MIMO-SDM) systems, allocating power among multiple transmit data streams improves the channel capacity. However, the optimum power allocation values are not always available in closed-form. For instance, when we use transmission and reception schemes that do not transfer the MIMO channel into parallel orthogonal channels (e.g., eigen-mode SDM), the signal to interference plus noise ratio (SINR) of each data stream at the output of the receiver is not proportional to its corresponding transmit power. This feature makes it difficult to obtain the optimal closed-form power allocation value for each data stream. Thus, in this paper, we propose an iterative power allocation scheme for MIMO-SDM systems where the SINR is not proportional to the transmit power. Furthermore, we incorporate a transmit antenna selection scheme into the proposed power allocation scheme in order to attain further capacity enhancement. Computer simulation results are provided to show the effectiveness of the proposed power allocation schemes.

Text detection in color images has become an active research area in the past few decades. In this paper, we present a novel approach to accurately detect text in color images possibly with a complex background. The proposed algorithm is based on the combination of connected component and texture feature analysis of unknown text region contours. First, we utilize an elaborate color image edge detection algorithm to extract all possible text edge pixels. Connected component analysis is performed on these edge pixels to detect the external contour and possible internal contours of potential text regions. The gradient and geometrical characteristics of each region contour are carefully examined to construct candidate text regions and classify part non-text regions. Then each candidate text region is verified with texture features derived from wavelet domain. Finally, the Expectation maximization algorithm is introduced to binarize each text region to prepare data for recognition. In contrast to previous approach, our algorithm combines both the efficiency of connected component based method and robustness of texture based analysis. Experimental results show that our proposed algorithm is robust in text detection with respect to different character size, orientation, color and language and can provide reliable text binarization result.

In this letter, we propose a new detection method for an OFDM signal distorted by IQ imbalance, and a pilot pattern to estimate the channel associated with IQ imbalance. It is shown by computer simulation that the proposed method can achieve robust detection even when severe IQ imbalance exists in OFDM systems with an input of higher-order constellation.

In this paper, we introduce a low computing post processing algorithm to simultaneously suppress blocking and ringing artifacts of compressed video sequences. A new regularization function to incorporate smoothness to neighboring pixels is defined, where the function is composed of four sub-functions combined with pixel-based data fidelity and smoothing terms. Therefore, the solution can be obtained without inverse matrix or vector-matrix computation, so that low complexity implementation is possible. In addition, the regularization parameter controlling the relative importance between the data fidelity and the degree of smoothness is estimated from the available overhead information in decoder, such as, macroblock type and quantization step size. The experimental results show the capability and efficiency of the proposed algorithm.

In this paper we propose a novel spatial fading simulator to evaluate the performance of an array antenna and show its spatial stochastic characteristics by computer simulation based on parameters verified by experimental data. We introduce a cavity-excited circular array (CECA) as a fading simulator that can simulate realistic mobile communication environments. To evaluate the antenna array, two stochastic characteristics are necessary. The first one is the fading phenomenon and the second is the angular spread (AS) of the incident wave. The computer simulation results with respect to fading and AS show that CECA works well as a spatial fading simulator for performance evaluation of an antenna array. We first present the basic structure, features and design methodology of CECA, and then show computer simulation results of the spatial stochastic characteristics. The results convince us that CECA is useful to evaluate performance of antenna arrays.

Random Early Detection (RED), an active queue management scheme, has been recommended by the Internet Engineering Task Force (IETF) for the next generation routers. RED suffers from a number of performance problems, such as low throughput, large delay/jitter, and induces instability in networks. Many of the previous attempts to improve the performance of RED have been based on optimizing the values of the RED parameters. However, results have shown that such optimizations resulted in limited improvement in the performance. In this paper, we propose Double Slope RED (DSRED), a new active queue management scheme to improve the performance of RED. The proposed scheme is based on dynamically changing the slope of the packet drop probability curve as a function of the level of congestion in the buffer. Results show that our proposed scheme results in better performance than original RED.

We propose a new receiving method for an information-providing system that uses LED-based traffic lights as the transmitter. We analyzed the improvements obtained when 2-dimentional image sensor replaced the conventional single-element photodiode. First, we discuss the maximum receiver's field of view (FOV) when using the 2-dimentional image sensor at a particular focal length. We analyzed the best vertical inclination for both lanes and quantified the improvements in terms of the enhancement of received signal-noise ratio (SNR) when different numbers of pixels were applied. Our results indicate that using more pixels increases the received SNR and the service area becomes wider compared to the conventional single-element system. Consequently, receivable information within the service area also increased. We also found that the optimum number of pixels to accomplish a reliable communication system is 5050 because performance degradation occured with a larger number of pixels.

Even though various kinds of IPv6 transition mechanisms have been developed for the transition to an IPv6 network, these transition mechanisms take no stance on whether applications support IPv6 or not. This paper describes why the transition period between IPv4 and IPv6 applications may not be straightforward and applications should be ported to support both IPv4 and IPv6; such applications are called "IP version-independent applications." Also, this paper examines and empirically evaluates overhead of the IP version-independent applications, since the performance implication is not well known. The overhead might be very dependent on data sizes and network performance, but it was relatively minimized for general Internet traffic with larger data sizes and lower network latency.

This paper presents a personal identification method based on BMME and LDA for images acquired at anterior and posterior occlusion expression of teeth. The method consists of teeth region extraction, BMME, and pattern recognition for the images acquired at the anterior and posterior occlusion state of teeth. Two occlusions can provide consistent teeth appearance in images and BMME can reduce matching error in pattern recognition. Using teeth images can be beneficial in recognition because teeth, rigid objects, cannot be deformed at the moment of image acquisition. In the experiments, the algorithm was successful in teeth recognition for personal identification for 20 people, which encouraged our method to be able to contribute to multi-modal authentication systems.

This letter analyzes the performance of statistical cooperative diversity based on space-time block codes (STBC) (Statistical STBC cooperative diversity) considering the effects of quantization and observation noise. Binary quantization is used. The bit error rate (BER) and average mutual information of the statistical STBC cooperative diversity with Alamouti's STBC and two active nodes are derived in the presence of general observation noise. It is shown that the performance of the statistical STBC cooperative diversity depends on the effects of observation noise and the number of cooperating nodes largely. It is also shown how much the communication between sensor nodes or feedback from the fusion center improves the performance of STBC cooperative diversity.

In 3G CDMA mobile communication systems, high data rate services are essential for many key applications. When an MS approaches the cell border, link performance is degraded and more power should be allocated to maintain the link performance. Since the maximum available signal power is limited, the link adaptation mechanism may diminish the data rate to maintain link performance. This implies that the valid coverage shrinks when the data rate increases. The shrinking of valid coverage under a predetermined data rate will strongly impact on the reliability of high data rate services. In this work, the encoded bit error probabilities of 3G CDMA mobile communication systems, over large-scale and large-small-scale fading channels, were analyzed based on SGA and SIGA methods. Analytic methods were also proposed to investigate the issues of coverage shrinking and service data rate variations. Furthermore, the outage probability, cell coverage percentage and the staying probabilities of available data rates were well examined. The proposed analytic methods can be applied, as a preliminary research, to the design of cellular-system-related techniques, such as QoS control, available data rate prediction, power reservation, and service adaptation.

Since their inception almost fifty years ago, hidden Markov models (HMMs) have have become the predominant methodology for automatic speech recognition (ASR) systems--today, most state-of-the-art speech systems are HMM-based. There have been a number of ways to explain HMMs and to list their capabilities, each of these ways having both advantages and disadvantages. In an effort to better understand what HMMs can do, this tutorial article analyzes HMMs by exploring a definition of HMMs in terms of random variables and conditional independence assumptions. We prefer this definition as it allows us to reason more throughly about the capabilities of HMMs. In particular, it is possible to deduce that there are, in theory at least, no limitations to the class of probability distributions representable by HMMs. This paper concludes that, in search of a model to supersede the HMM (say for ASR), rather than trying to correct for HMM limitations in the general case, new models should be found based on their potential for better parsimony, computational requirements, and noise insensitivity.

In this paper, we provide a practical formulation of the problem of identifying all error occurrences and all failed scan cells in at-speed scan based BIST environment. We propose a method that can be used to identify every error when the circuit test frequency is higher than the tester frequency. Our approach requires very little extra hardware for diagnosis and the test application time required to identify errors is a linear function of the frequency ratio between the CUT and the tester.

In this letter, we propose a partial minimum mean-squared error (MMSE) with successive interference cancellation (PMMSESIC) method in frequency domain to mitigate ICI caused by channel variation. Each detection, the proposed method detects the symbol with the largest received signal-to-interference-plus-noise ratio (SINR) among all the undetected symbols, using an MMSE detector that considers only the interference of several neithborhood subcarriers. Analysis and simulations show that it outperforms the MMSE method at relatively high Eb/N0 and its performance is close to the MMSE with successive detection (MMSESD) method in relatively low Doppler frequency region.

In this letter, a new formula is proposed for calculating the polarization entropy, based on the least square method. There is no need to calculate the eigenvalues of a covariance matrix as well as to use logarithms of values. So the time for computing the polarization entropy is reduced. Using polarimetric SAR data, the authors validate the effectiveness of the new formula.