MIMO spatial multiplexing systems are attracting a lot of attention because of their high spectral effiencies. Maximum Likelihood Detection (MLD) is known to be the optimal signal detection method for MIMO spatial multiplexing systems in terms of bit error rate (BER). However, the main drawback of MLD is its high complexity. In this paper, to reduce the computational complexity of MLD and to attain good BER in MIMO spatial multiplexing systems, we propose the minimum mean square error (MMSE)-MLD that combines MMSE detection and MLD according to the estimated SINR from each transmit antenna. We also propose the ordered successive MMSE detection (OSD)-MLD that combines OSD and MLD according to the estimated SINR from the transmit antennas. Simulation results show that the proposed MMSE-MLD and OSD-MLD can attain almost identical BER to that of MLD but with less complexity.

In this letter, a hybrid selection combining (SC) and maximal ratio receive combining (MRRC) technique is proposed for orthogonal frequency-division multiplexing (OFDM) systems with multiple receive antennas. The proposed technique still uses multiple receive antennas, but it has just a single RF front-end and a single baseband demodulator. In comparison with the OFDM system with no diversity, we can achieve superior gain irrespective of bandwidth efficiency, and also in comparison with the MRRC OFDM, we can achieve better gain under the bandwidth efficiency of 3 bps/Hz at the bit error rate of 10-6.

A novel mobile assignment method based on transmit power and cell load is proposed for WCDMA base station location planning. Experimental results show that, compared with the currently widely used mobile assignment method based on link attenuation, the proposed mobile assignment method is more reasonable and unnecessary base stations are reduced in the planning results.

The electrical parameters (88 LRGC matrices) of 8-coupled uniform lossy transmission lines were extracted from 40 S-parameter values measured by using 2-port VNA measurements, where all the ports other than 2 VNA ports were terminated by 50 ohm chip resistors. It was assumed in the extraction step that the transmission lines are weakly-coupled, and that the resistance values of all the termination chip resistors are exactly 50 ohms with the second reflections neglected. Comparison of the extracted LRGC matrix components with those from a commercial 3D field solver revealed on average and a maximum relative difference of 2.45% and 7.66%, respectively. In addition, the time-domain crosstalk voltage waveforms in the measured data and those in the SPICE simulation results using the extracted LRGC parameters agreed very well with the average difference and the maximum relative difference in peak crosstalk voltages of 4.15% and 9.68%, respectively.

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.

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.

This paper presents a new bearing fault detection algorithm based on analyzing singular points of vibration signals using the Haar wavelet. The proposed Haar Fault Detection (HFD) algorithm is compared with a previously-developed algorithm associated with the Morlet wavelet. We also substitute the Haar wavelet with Daubechies wavelets with larger compact supports and evaluate the results. Simulations carried on real data demonstrate that the HFD algorithm achieves a comparable accuracy while having a lower computational cost. This makes the HFD algorithm an appropriate candidate for fast processing of bearing faults.

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.

As the technology scaling approaching nano-scale region, variability in device performance becomes a major issue in the design of integrated circuits. Besides the growing amount of variability, the statistical nature of the variability is changing as the progress of technology generation. In the past, die-to-die variability, which is well managed by the worst case design technique, dominates over within-die variability. In present and the future, the amount of within-die variability is increasing and it casts a challenge in design methodology. This paper first shows measured results of variability in three different processes of 0.35, 0.18, and 0.13 µm technologies, and explains the above mentioned trend of variability. An example of modeling for the within-die variability is explained. The impact of within-die random variability on circuit performance is demonstrated using a simple numerical example. It shows that a circuit that is designed optimally under the assumption of deterministic delay is now most susceptible to random fluctuation in delay, which clearly indicates the requirement of statistical design methodology.

In this paper, we propose alternate self shielding to remove critical transitions of on-chip global interconnect. Our proposed method alternates shield and signal wires cycle by cycle. The conventional self-shielding methods need additional wires to remove critical transition by encoding. The proposed alternate self-shielding, however, requires no additional wires. We evaluate our method by simulating signal transimission with a circuit simulator. As a result, our proposed method is superior in bit rate compared to others from 10% to 75%.

Shadow detection and removal is important to deal with traffic image sequences. Cast shadow of vehicle may lead to an inaccurate object feature extraction and erroneous scene analysis. Furthermore, separate vehicles can be connected through shadow. Both can confuse object recognition systems. In this paper, a robust method is proposed for detecting and removing active cast shadow in monocular color image sequences. Background subtraction method is used to extract moving blobs in color and gradient dimensions, and the YCrCb color space is adopted for detecting and removing the cast shadow. Even when shadows link different vehicles, it can detect the each vehicle figure using modified mask by shadow bar. Experimental results from town scenes show that proposed method is effective and the classification accuracy is sufficient for general vehicle type classification.

Recently, a novel detector was proposed by the authors for code acquisition in non-Gaussian impulsive channels [3], which dramatically outperforms the conventional squared-sum detector; however, it requires exact knowledge of the non-Gaussian noise dispersion. In this paper, a robust detector is proposed, which employs the signs and ranks of the received signal samples, instead of their actual values, and so does not require knowledge of the non-Gaussian noise dispersion. The acquisition performance of the proposed detector is compared with that of the detector of [3] in terms of the mean acquisition time. The simulation results show that the proposed scheme is not only robust to deviations from the true value of the non-Gaussian noise dispersion, but also has comparable performance to that of the scheme of [3] using exact knowledge of the non-Gaussian noise dispersion.

There are many system proposals for satellite-based broadband communications that promise high capacity and ease of access. Many of these proposals require advanced switching technology and signal processing on-board the satellite(s). One solution is based on a geo-synchronous (GEO) satellite system equipped with on-board processing and on-board switching. An important feature of this system is allowing for a maximum number of simultaneous users, hence, requiring effective medium access control (MAC) layer protocols for connection admission control (CAC) and bandwidth on demand (BoD) algorithms. In this paper, an integrated CAC and BoD algorithm is proposed for a broadband satellite communication system with heterogeneous traffic. A detailed modeling and simulation approach is presented for performance evaluation of the integrated CAC and BoD algorithm based on heterogeneous traffic types. The proposed CAC and BoD scheme is shown to be able to efficiently utilize available bandwidth and to gain high throughput, and also to maintain good Grade of Service (GoS) for all the traffic types. The end-to-end delay for real-time traffic in the system falls well within ITU's Quality of Service (QoS) specification for GEO-based satellite systems.

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.

A new wideband signal DOA estimation algorithm based on modified quantum genetic algorithm (MQGA) is proposed in the presence of the errors and the mutual coupling between array elements. In the algorithm, the narrowband signal subspace fitting method is generalized to wideband signal DOA finding according to the character of space spectrum of wideband signal, and so the rule function is constructed. Then, the solutions is encoded onto chromosomes as a string of binary sequence, the variable quantum rotation angle is defined according to the distribution of optimization solutions. Finally, we use the MQGA algorithm to solve the nonlinear global azimuths optimization problem, and get optimization azimuths by fitness values. The computer simulation results illustrated that the new algorithm have good estimation performance.

In recent years, the number of studies investigating new directions in speech modeling that goes beyond the conventional HMM has increased considerably. One promising approach is to use Bayesian Networks (BN) as speech models. Full recognition systems based on Dynamic BN as well as acoustic models using BN have been proposed lately. Our group at ATR has been developing a hybrid HMM/BN model, which is an HMM where the state probability distribution is modeled by a BN, instead of commonly used mixtures of Gaussian functions. In this paper, we describe how to use the hybrid HMM/BN acoustic models, especially emphasizing some design and implementation issues. The most essential part of HMM/BN model building is the choice of the state BN topology. As it is manually chosen, there are some factors that should be considered in this process. They include, but are not limited to, the type of data, the task and the available additional information. When context-dependent models are used, the state-level structure can be obtained by traditional methods. The HMM/BN parameter learning is based on the Viterbi training paradigm and consists of two alternating steps - BN training and HMM transition updates. For recognition, in some cases, BN inference is computationally equivalent to a mixture of Gaussians, which allows HMM/BN model to be used in existing decoders without any modification. We present two examples of HMM/BN model applications in speech recognition systems. Evaluations under various conditions and for different tasks showed that the HMM/BN model gives consistently better performance than the conventional HMM.

In PKC'01, Tzeng et al. proposed two robust forward-secure signature schemes with proactive security: one is an efficient scheme, but it requires a manager; the other scheme is a new construction based on distributed multiplication procedures. In this paper, we point out their new distributed multiplication procedure is not secure, thus making the whole new construction insecure. Finally, we present an improved forward-secure signature scheme without a manager.

We introduce a robust classification method based on the Bayesian predictive distribution (Bayesian Predictive Classification, referred to as BPC) for speech recognition. We and others have recently proposed a total Bayesian framework named Variational Bayesian Estimation and Clustering for speech recognition (VBEC). VBEC includes the practical computation of approximate posterior distributions that are essential for BPC, based on variational Bayes (VB). BPC using VB posterior distributions (VB-BPC) provides an analytical solution for the predictive distribution as the Student's t-distribution, which can mitigate the over-training effects by marginalizing the model parameters of an output distribution. We address the sparse data problem in speech recognition, and show experimentally that VB-BPC is robust against data sparseness.

This paper describes a method of generating F0 contours from natural F0 segmental shapes for speech synthesis. The extracted shapes of the F0 units are basically held invariant by eliminating any averaging operations in the analysis phase and by minimizing modification operations in the synthesis phase. The use of natural F0 shapes has great potential to cover a wide variety of speaking styles with the same framework, including not only read-aloud speech, but also dialogues and emotional speech. A linear-regression statistical model is used to "manipulate" the stored raw F0 shapes to build them up into a sentential F0 contour. Through experimental evaluations, the proposed model is shown to provide stable and robust F0 contour prediction for various speakers. By using this model, linguistically derived information about a sentence can be directly mapped, in a purely data-driven manner, to acoustic F0 values of the sentential intonation contour for a given target speaker.

Development of a hidden Markov model (HMM)-based Korean speech synthesis system and its evaluation is described. Statistical HMM models for Korean speech units are trained with the hand-labeled speech database including the contextual information about phoneme, morpheme, word phrase, utterance, and break strength. The developed system produced speech with a fairly good prosody. The synthesized speech is evaluated and compared with that of our corpus-based unit concatenating Korean text-to-speech system. The two systems were trained with the same manually labeled speech database.