An adaptive method analyzing analytic speech signals is proposed in this paper. The method decreases the errors of finite precision on calculation in a method with real coefficients. It is shown from the results of experiments that the proposed method is more useful than adaptive methods with real coefficients.

This paper presents a new multi-clustering network for the purpose of intelligent data classification. In this network, the first layer is a self-organized clustering layer and the second layer is a restricted clustering layer with a neighborhood mechanism. A new clustering algorithm is developed in this system for the efficiently use of parallel processors. This parallel algorithm enables the nodes of this network to be independently processed in order to minimize data communication load among processors. Using the parallel processors, the quite low calculation cost can be realized among the conventional networks. For example, a 4-processor parallel computing system has shown its ability to reduce the time taken for data classification to 26.75% of a single processor system without declining its performance.

In recent years, much research interest has developed in image enhancement and haze removal techniques. With increasing demand for real time enhancement and haze removal, the need for efficient architecture incorporating both haze removal and enhancement is necessary. In this paper, we propose an architecture supporting both real-time Retinex-based image enhancement and haze removal, using a single module. Efficiently leveraging the similarity between Retinex-based image enhancement and haze removal algorithms, we have successfully proposed an architecture supporting both using a single module. The implementation results reveal that just 1% logic circuits overhead is required to support Retinex-based image enhancement in single mode and haze removal based on Retinex model. This reduction in computation complexity by using a single module reduces the processing and memory implications especially in mobile consumer electronics, as opposed to implementing them individually using different modules. Furthermore, we utilize image enhancement for transmission map estimation instead of soft matting, thereby avoiding further computation complexity which would affect our goal of realizing high frame-rate real time processing. Our FPGA implementation, operating at an optimum frequency of 125MHz with 5.67M total block memory bit size, supports WUXGA (1,920×1,200) 60fps as well as 1080p60 color input. Our proposed design is competitive with existing state-of-the-art designs. Our proposal is tailored to enhance consumer electronic such as on-board cameras, active surveillance intrusion detection systems, autonomous cars, mobile streaming systems and robotics with low processing and memory requirements.

This paper introduces some modelling methods of time-varying stochastic process and its linear/nonlinear adaptive identification. Time-varying models are often identified by using a least square criterion. However the criterion should assume a time invariant stochastic model and infinite observed data. In order to adjust these serious different assumptions, some windowing techniques are introduced. Although the windows are usually applied to a batch processing of parameter estimates, all adaptive methods should also consider them at difference point of view. In this paper, two typical windowing techniques are explained into adaptive processing. In addition to the use of windows, time-varying stochastic ARMA models are built with these criterions and windows. By using these criterions and models, this paper explains nonlinear parameter estimation and the property of estimation convergence. On these discussions, some approaches are introduced, i.e., sophisticated stochastic modelling and multi-rate processing.

In this paper, a new database suitable for HMM-based automatic Filipino speech recognition is described for the purpose of training a domain-independent, large-vocabulary continuous speech recognition system. Although it is known that high-performance speech recognition systems depend on a superior speech database used in the training stage, due to the lack of such an appropriate database, previous reports on Filipino speech recognition had to contend with serious data sparsity issues. In this paper we alleviate such sparsity through appropriate data analysis that makes the evaluation results more reliable. The best system is identified through its low word-error rate to a cross-validation set containing almost three hours of unknown speech data. Language-dependent problems are discussed, and their impact on accuracy was analyzed. The approach is currently data driven, however it serves as a competent baseline model for succeeding future developments.

Genetic algorithm (GA) is now an important tool in the field of wireless communications. For multiple-input/multiple-output (MIMO) wireless communications system employing spatial multiplexing transmission, we evaluate the effects of GA parameters value on channel parameters in fading channels. We assume transmit-correlated Rayleigh and Rician fading with realistic Laplacian power azimuth spectrum. Azimuth spread (AS) and Rician K-factor are selected according to the measurement-based WINNER II channel model for several scenarios. Herein we have shown the effects of GA parameters and channel parameters in different WINNER II scenarios (i.e., AS and K values) and rank of the deterministic components. We employ meta GA that suitably selects the population (P), generation (G) and mutation probability (pm) for the inner GA. Then we show the cumulative distribution function (CDF) obtain experimentally for the condition number C of the channel matrix H. It is found that, GA parameters depend on the channel parameters, i.e., GA parameters are the functions of the channel parameters. It is also found that for the poorer channel conditions smaller GA parameter values are required for MIMO detection. This approach will help to achieve maximum performance in practical condition for the lower numerical complexity.

In recent years, fractal processes have played important roles in various application fields. Since a 1/f process possesses the statistical self-similarity, it is considered sa a main part of fractal signal modeling. On the other hand, noise reduction is often needed in real-world signal processing. Hence, we propose an enhancement algorithm for 1/f signal disturbed by white noise. The algorithm is based on constrained minimization in a wavelet domain: the power of 1/f signal distortion in the wavelet domain is minimized under a constraint that the power of residual noise in the wavelet domain is smaller than a threshold level. We solve this constrained minimization problem using a Lagrangian equation. We also consider a setting method of the Lagrange multiplier in the proposed algorithm. In addition, we will confirm that the proposed algorithm with this Lagrange multiplier setting method obtains better enhancement results than the conventional algorithm through computer simulations.

In this paper, we propose a novel architecture for large-scale regular expression matching, called dynamically reconfigurable bit-parallel NFA architecture (Dynamic BP-NFA), which allows dynamic loading of regular expressions on-the-fly as well as efficient pattern matching for fast data streams. This is the first dynamically reconfigurable hardware with guaranteed performance for the class of extended patterns, which is a subclass of regular expressions consisting of union of characters and its repeat. This class allows operators such as character classes, gaps, optional characters, and bounded and unbounded repeats of character classes. The key to our architecture is the use of bit-parallel pattern matching approach, in which the information of an input non-deterministic finite automaton (NFA) is first compactly encoded in bit-masks stored in a collection of registers and block RAMs. Then, the NFA is efficiently simulated by a fixed circuitry using bitwise Boolean and arithmetic operations consuming one input character per clock regardless of the actual contents of an input text. Experimental results showed that our hardwares for both string and extended patterns were comparable to previous dynamically reconfigurable hardwares in their performances.

This paper presents a VLSI architecture of MMSE detection in a 44 MIMO-OFDM receiver. Packet-based MIMO-OFDM imposes a considerable throughput requirement on the matrix inversion because of strict timing in frame structure and subcarrier-by-subcarrier basis processing. Pipeline processing oriented algorithms are preferable to tackle this issue. We propose a pipelined MMSE detector using Strassen's algorithms of matrix inversion and multiplication. This circuit achieves real-time operation which does not depend on numbers of subcarriers. The designed circuit has been implemented to a 90-nm CMOS process and shows a potential for providing a 2.6-Gbps transmission speed in a 160-MHz signal bandwidth.

The IEEE802.11ac task group has announced the use of a wider channel that extends the channel bandwidth to more than 80 MHz. We present an experimental platform consisting of a baseband and a RF unit in a 22 MIMO-OFDM system for the wider channel and report its system performance results from a field experiment. The MIMO-OFDM transceiver in the baseband unit has been designed to detect real-time MIMO and provides a maximum data rate of 600 Mbps. OFDM tends to cause high peak PAPR for wider channels and distorts the power amplifier performance in the RF unit. We have improved the non-linear distortion by optimizing the OFDM preamble and evaluated its performance by conducting a simulation integrated with baseband processing and a RF. In the field experiment, our platform tested the communication performance in a farm and a passage environment.

In this paper, we propose a WiFi-based indoor positioning system using a fingerprint method, whose database is constructed with estimated reference locations. The reference locations and their information, called data sets in this paper, are obtained by moving reference devices at a constant speed while gathering information of available access points (APs). In this approach, the reference locations can be estimated using the velocity without any precise reference location information. Therefore, the cost of database construction can be dramatically reduced. However, each data set includes some errors due to such as the fluctuation of received signal strength indicator (RSSI) values, the device-specific WiFi sensitivities, the AP installations, and removals. In this paper, we propose a method to merge data sets to construct a consistent database suppressing such undesired effects. The proposed approach assumes that the intervals of reference locations in the database are constant and that the fingerprint for each reference location is calculated from multiple data sets. Through experimental results, we reveal that our approach can achieve an accuracy of 80%. We also show a detailed discussion on the results related parameters in the proposed approach.

In this paper, we introduce a flexible design for intellectual property(IP) which has become important to design system LSI. The proposed IPs which have high flexibility for user requirement. The design priority is determined by setting parameters as the number of arithmetic unit, internal bitlength, clock speed and so on. The design time can thus be reduced. Designed IP is based on the reconfigurable architecture in which many structures can be dynamically selected. This paper shows a implementation of Frequency Response Masking digital filter(FRM) and Principal Components Analysis(PCA) using a reconfigurable architecture. We show the method to realize the designed circuit and the results of experiments using field programmable gate array(FPGA).

This paper describes a noise robustness technique that normalizes the cepstral amplitude range in order to remove the influence of additive noise. Additive noise causes speech feature mismatches between testing and training environments and it degrades recognition accuracy in noisy environments. We presume an approximate model that expresses the influence by changing the amplitude range and the DC component in the log-spectra. According to this model, we propose a cepstral amplitude range normalization (CARN) that normalizes the cepstral distance between maximum and minimum values. It can estimate noise robust features without prior knowledge or adaptation. We evaluated its performance in an isolated word recognition task by using the Noisex92 database. Compared with the combinations of conventional methods, the CARN could improve recognition accuracy under various SNR conditions.

This paper proposes a nonlinear signal processing by using a three layered network which is trained with self-organized clustering and supervised learning. The network consists of three layers, i.e., self-organized layer, an evaluation layer and an output layer. Since the evaluation layer is designed as a simple perceptron network and the output layer is designed as a fixed weight linear node, the training complexity is the same as a conventional one consisting of self-organized clustering and a simple perceptron network. In other words, quite high speed training can be realized. Generally speaking, since the data range is arbitrary large in signal procession, the network shoulk cover this range and output a value as accurately as possible. However, it may be hard for only a node in the network to output these data. Instead of this mechanism, if this dynamic range is covered by using several nodes, the complexity of each node is reduced and the associated range is also limited. This results on the higher performance of the network than conventional RBFs. This paper introduces a new non-linear spectrum estimation which consists of LPC analysis and RBF network. It is shown that accuracy spectrum envelopes can be obtained since a new RBF network can estimate some nonlinearities in a speech production.

In this paper, we propose a new PAPR reduction by using the hybrid of a partial transmit sequences (PTS) and an adaptive peak power reduction (APPR) methods with coded side information (SI) technique. These methods are used in an Orthogonal Frequency Division Multiplexing (OFDM) system. The OFDM employs orthogonal sub-carriers for data modulation. These sub-carriers unexpectedly present a large Peak to Average Power Ratio (PAPR) in some cases. In order to reduce PAPR, the sequence of input data is rearranged by PTS. The APPR method is also used to controls the peak level of modulation signals by an adaptive algorithm. A proposed reduction method consists of these two methods and realizes both advantages at the same time. In order to make the optimum condition on PTS for PAPR reduction, a quite large calculation cost must be demanded and thus it is impossible to obtain the optimum PTS. In the proposed method, by using the pseudo-optimum condition with a coded SI technique, the total calculation cost becomes drastically reduced. In simulation results, the proposed method shows the improvement on PAPR and also reveals the high performance on bit error rate (BER) of an OFDM system.

We present area- and power-efficient pipeline 128- and 128/64-point fast Fourier transform (FFT) processors for 8x8 multiple-input multiple-output orthogonal frequency multiplexing (MIMO-OFDM) systems based on the specification framework of IEEE 802.11ac WLANs. Our new FFT processors use mixed-radix multipath delay commutator (MRMDC) architecture from the point of view of low complexity and high memory use. A conventional MRMDC architecture induces large circuits in delay commutators, which change the order of data sequences for the butterfly units. The proposed architecture replaces delay elements with new commutators that cooperate with other MIMO-OFDM processing blocks. These commutators are inserted in the front and rear of the input and output memory units. Our FFT processors exhibit a 50–51% reduction in logic gates and 70–72% reduction in power dissipation as compared with conventional ones.

We propose an auto-mesh generation algorithm for 3-Dimensional elliptic model on acoustic analysis of the vocal tract. We mesh the vocal tract and compute the vocal tract transfer function (VTTF) using Finite Element Method (FEM). We show there is little difference between the VTTF using our algorithm and that of the manual mesh, especially for vowel /a/. We show that the number of nodes is depended on the shape of the cross section of the vocal tract. Furthermore we compute the VTTF of the vocal tract with variable shape continuously.