Functional Electrical Stimulation (FES) can be effective in assisting or restoring paralyzed motor functions. The purpose of this study is to examine experimentally the fuzzy controller based on cycle-to-cycle control for FES-induced gait. A basic experimental test was performed on controlling maximum knee extension angle with normal subjects. In most of control trials, the joint angle was controlled well compensating changes in muscle responses to electrical stimulation. The results show that the fuzzy controller would be practical in clinical applications of gait control by FES. An automatic parameter tuning would be required practically for quick responses in reaching the target and in compensating the change in muscle responses without causing oscillating responses.

Optimal saving in TCAM search power can be achieved with the combined strategy of both hardware-based techniques and a power friendly TCAM configuration. This letter proposes that a conditional precharging hardware scheme is used with a power aware TCAM configuration. In the traffic simulation results, the proposed scheme conservatively saved 72% of energy with unbiased traffic compared to no energy saving schemes for a sample design of 51272 TCAM block.

This paper investigates the fixed-slope lossy coding of individual sequences and nonstationary sources. We clarify that, for a given individual sequence, the optimal cost attainable by the blockwise lossy encoders is equal to the optimal average cost with respect to the empirical distribution of the given sequence. Moreover, we show that, for a given nonstationary source, the optimal cost attainable by the blockwise encoders is equal to the supremum of the optimal average cost over all the stationary sources in the stationary hull of the given source. In addition, we show that the universal lossy coding algorithm based on Lempel-Ziv 78 code attains the optimal cost for any individual sequence and any nonstationary source.

We present an adaptive dynamic load balancing scheme for 3D texture based sort-last parallel volume rendering on a PC cluster equipped with GPUs. Our scheme exploits not only task parallelism but also data parallelism during rendering by combining the hierarchical data structures (octree and parallel BSP tree) in order to skip empty regions and distribute proper workloads to rendering nodes. Our scheme can also conduct a valid parallel rendering and image compositing in visibility order by employing a 3D clustering algorithm. To alleviate the imbalance when the transfer function is changed, a load rebalancing is inexpensively supported by exchanging only needed data. A detailed performance analysis is provided and scaling characteristics of our scheme are discussed. These show that our scheme can achieve significant performance gains by increasing parallelism and decreasing synchronizing costs compared to the traditional static distribution schemes.

We have developed the fabrication process, the circuit design technology, and the cryopackaging technology for high-Tc single flux quantum (SFQ) devices with the aim of application to an analog-to-digital (A/D) converter circuit for future wireless communication and a sampler system for high-speed measurements. Reproducibility of fabricating ramp-edge Josephson junctions with IcRn products above 1 mV at 40 K and small Ic spreads on a superconducting groundplane was much improved by employing smooth multilayer structures and optimizing the junction fabrication process. The separated base-electrode layout (SBL) method that suppresses the Jc spread for interface-modified junctions in circuits was developed. This method enabled low-frequency logic operations of various elementary SFQ circuits with relatively wide bias current margins and operation of a toggle-flip-flop (T-FF) above 200 GHz at 40 K. Operation of a 1:2 demultiplexer, one of main elements of a hybrid-type Σ-Δ A/D converter circuit, was also demonstrated. We developed a sampler system in which a sampler circuit with a potential bandwidth over 100 GHz was cooled by a compact stirling cooler, and waveform observation experiments confirmed the actual system bandwidth well over 50 GHz.

A sweep spectrum analyzer has been improved over the years, but the fundamental method has not been changed before the 'Super Sweep' method appeared. The 'Super Sweep' method has been expected to break the limitation of the conventional sweep spectrum analyzer, a limit of the maximum sweep rate which is in inverse proportion to the square of the frequency resolution. The superior performance of the 'Super Sweep' method, however, has not been experimentally proved yet. This paper gives the experimental evaluation on the 'Super Sweep' spectrum analyzer, of which theoretical concepts have already been presented by the authors of this paper. Before giving the experimental results, we give complete analysis for a sweep spectrum analyzer and express the principle of the super-sweep operation with a complete set of equations. We developed an experimental system whose components operated in an optimum condition as the spectrum analyzer. Then we investigated its properties, a peak level reduction and broadening of the frequency resolution of the measured spectrum, by changing the sweep rate. We also confirmed that the experimental system satisfactorily detected the spectrum at least 30 times faster than the conventional method and the sweep rate was in proportion to the bandwidth of the base band signal to be analyzed. We proved that the 'Super Sweep' method broke the restriction of the sweep rate put on a conventional sweep spectrum analyzer.

We present a novel method of integrating the likelihoods of multiple feature streams, representing different acoustic aspects, for robust speech recognition. The integration algorithm dynamically calculates a frame-wise stream weight so that a higher weight is given to a stream that is robust to a variety of noisy environments or speaking styles. Such a robust stream is expected to show discriminative ability. A conventional method proposed for the recognition of spoken digits calculates the weights from the entropy of the whole set of HMM states. This paper extends the dynamic weighting to a real-time large-vocabulary continuous speech recognition (LVCSR) system. The proposed weight is calculated in real-time from mutual information between an input stream and active HMM states in a search space without an additional likelihood calculation. Furthermore, the mutual information takes the width of the search space into account by calculating the marginal entropy from the number of active states. In this paper, we integrate three features that are extracted through auditory filters by taking into account the human auditory system's ability to extract amplitude and frequency modulations. Due to this, features representing energy, amplitude drift, and resonant frequency drifts, are integrated. These features are expected to provide complementary clues for speech recognition. Speech recognition experiments on field reports and spontaneous commentary from Japanese broadcast news showed that the proposed method reduced error words by 9.2% in field reports and 4.7% in spontaneous commentaries relative to the best result obtained from a single stream.

We propose a stability-guaranteed width control (SGWC) for the hot strip finishing mill. It is shown that the proposed SGWC guarantees the stability of the width controller by the universal approximation of the neural network. It is shown through the field test in the hot strip mill of POSCO that the stability of the width controller is guaranteed by the proposed control scheme.

Chinese is a representative tonal language, and it has been an attractive topic of how to process tone information in the state-of-the-art large vocabulary speech recognition system. This paper presents a novel way to derive an efficient phoneme set of tone-dependent units to build a recognition system, by iteratively merging a pair of tone-dependent units according to the principle of minimal loss of the Mutual Information (MI). The mutual information is measured between the word tokens and their phoneme transcriptions in a training text corpus, based on the system lexical and language model. The approach has a capability to keep discriminative tonal (and phoneme) contrasts that are most helpful for disambiguating homophone words due to lack of tones, and merge those tonal (and phoneme) contrasts that are not important for word disambiguation for the recognition task. This enables a flexible selection of phoneme set according to a balance between the MI information amount and the number of phonemes. We applied the method to traditional phoneme set of Initial/Finals, and derived several phoneme sets with different number of units. Speech recognition experiments using the derived sets showed its effectiveness.

In order to achieve the prioritized quality of service (QoS) guarantee, the IEEE 802.11e EDCAF (the enhanced distributed channel access function) provides the distinguished services by configuring the different QoS parameters to different access categories (ACs). An admission control scheme is needed to maximize the utilization of wireless channel. Most of papers study throughput improvement by solving the complicated multidimensional Markov-chain model. In this paper, we introduce a backoff model to study the transmission probability of the different arbitration interframe space number (AIFSN) and the minimum contention window size (CWmin). We propose an adaptive control scheme (ACS) to dynamically update AIFSN and CWmin based on the periodical monitoring of current channel status and QoS requirements to achieve the specific service differentiation at access points (AP). This paper provides an effective tuning mechanism for improving QoS in WLAN. Analytical and simulation results show that the proposed scheme outperforms the basic EDCAF in terms of throughput and service differentiation especially at high collision rate.

Formulae required for accurate approximate calculation of transition probabilities of embedded Markov chain for single-server queues of the GI/ M/1,GI/M/1/K,M/G/1,M/G/1/K type with heavy-tail lognormal distribution of inter-arrival or service time are given.

A lightweight and high gain planar antenna for space use is realized with radial waveguide slotted array and honeycomb structure with the weight of 1.16 kg and the diameter of 920.5 mm. The slot coupling is analyzed by method of moments considering the hybrid mode in the multi-layer waveguide structure. The propagation constant of the honeycomb structure is measured and the low-loss property is obtained at the frequency range of 8 GHz. The fabricated RLSA is measured and the reflection is around -10 dB in 8 GHz band. The measured aperture fields agree with the calculation in the radial direction. In the azimuthal direction, on the other hand, the fields show ripples of 6 dB and 60 degree. The gain of 35.9 dBi with the efficiency of 58.7% is obtained at 8.6 GHz.

This paper presents a new method for classification of underwater transient signals, which employs a binary image pattern of the mel-frequency cepstral coefficients as a feature vector and a feed-forward neural network as a classifier. The feature vector is obtained by taking DCT and 1-bit quantization for the square matrix of the mel-frequency cepstral coefficients that is derived from the frame based cepstral analysis. The classifier is a feed-forward neural network having one hidden layer and one output layer, and a back propagation algorithm is used to update the weighting vector of each layer. Experimental results with underwater transient signals demonstrate that the proposed method is very promising for classification of underwater transient signals.

We present a watermarking scheme that combines data compression and encryption in application to radiological medical images. In this approach we combine the image moment theory and image homogeneity in order to recover the watermark after a geometrical distortion. Image quality is measured with metrics used in image processing, such as PSNR and MSE.

A new adaptive filter algorithm based on the linear prediction property of sinusoidal signals is proposed for unbiased estimation of the frequency of a real tone in white noise. Similar to the least mean square algorithm, the estimator is computationally simple and it provides unbiased as well as direct frequency measurements. Learning behavior and variance of the estimated frequency are derived and confirmed by computer simulations.

This paper proposes a likelihood estimation method for reduced-complexity maximum-likelihood (ML) detectors in a multiple-input multiple-output (MIMO) spatial-multiplexing (SM) system. Reduced-complexity ML detectors, e.g., Sphere Decoder (SD) and QR decomposition (QRD)-M algorithm, are very promising as MIMO detectors because they can estimate the ML or a quasi-ML symbol with very low computational complexity. However, they may lose likelihood information about signal vectors having the opposite bit to the hard decision and bit error rate performance of the reduced-complexity ML detectors are inferior to that of the ML detector when soft-decision decoding is employed. This paper proposes a simple estimation method of the lost likelihood information suitable for the reduced-complexity ML detectors. The proposed likelihood estimation method is applicable to any reduced-complexity ML detectors and produces accurate soft-decision bits. Computer simulation confirms that the proposed method provides excellent decoding performance, keeping the advantage of low computational cost of the reduced-complexity ML detectors.

In recent decades, there has been a great deal of research into the problem of bilingual speech recognition - to develop a recognizer that can handle inter- and intra-sentential language switching between two languages. This paper presents our recent work on the development of a grammar-constrained, Mandarin-English bilingual Speech Recognition System (MESRS) for real world music retrieval. Two of the main difficult issues in handling the bilingual speech recognition systems for real world applications are tackled in this paper. One is to balance the performance and the complexity of the bilingual speech recognition system; the other is to effectively deal with the matrix language accents in embedded language. In order to process the intra-sentential language switching and reduce the amount of data required to robustly estimate statistical models, a compact single set of bilingual acoustic models derived by phone set merging and clustering is developed instead of using two separate monolingual models for each language. In our study, a novel Two-pass phone clustering method based on Confusion Matrix (TCM) is presented and compared with the log-likelihood measure method. Experiments testify that TCM can achieve better performance. Since potential system users' native language is Mandarin which is regarded as a matrix language in our application, their pronunciations of English as the embedded language usually contain Mandarin accents. In order to deal with the matrix language accents in embedded language, different non-native adaptation approaches are investigated. Experiments show that model retraining method outperforms the other common adaptation methods such as Maximum A Posteriori (MAP). With the effective incorporation of approaches on phone clustering and non-native adaptation, the Phrase Error Rate (PER) of MESRS for English utterances was reduced by 24.47% relatively compared to the baseline monolingual English system while the PER on Mandarin utterances was comparable to that of the baseline monolingual Mandarin system. The performance for bilingual utterances achieved 22.37% relative PER reduction.

Learning for boltzmann machines deals with each state individually. If given data is categorized, the probabilities have to be distributed to each state, not to each catetory. We propose boltzmann machines identifying the states in the same categories. Boltzmann machines with hidden units are the special cases. Boltzmann learning and em algorithm are effective learning methods for boltzmann machines. We solve boltzmann learning and em algorithm for the proposed models.

In this paper, a block-constrained trellis coded quantization (BC-TCQ) algorithm is combined with an algebraic codebook to produce an algebraic trellis code (ATC) to be used in ACELP coding. In ATC, the set of allowed algebraic codebook pulse positions is expanded, and the expanded set is partitioned into subsets of pulse positions; the trellis branches are labeled with these subsets. The list Viterbi algorithm (LVA) is used to select the excitation codevector. The combination of an ATC codebook and LVA trellis search algorithm is denoted as an ATC-LVA block code. The ATC-LVA block code is used as the fixed codebook of the AMR-WB 8.85 kbps mode, reducing complexity compared to the conventional algebraic codebook.

We have designed a superconductor-semiconductor hybrid analog-to-digital (A/D) converter and experimentally evaluated its performance at sampling frequencies up to 18.6 GHz. The A/D converter consists of a superconductor front-end circuit and a semiconductor back-end circuit. The front-end circuit includes a sigma-delta modulator and an interface circuit, which is for transmitting data signal to the semiconductor back-end circuit. The semiconductor back-end circuit performs decimation filtering. The design of the modulator was modified to reduce effects of integrator leak and thermal noise on signal-to-noise ratio (SNR). Using the improved modulator design, we achieved a bit-accuracy close to the ideal value. The hybrid architecture enabled us to reduce the integration scale of the front-end circuit to fewer than 500 junctions. This simplicity makes feasible a circuit based on a high TC superconductor as well as on a low TC superconductor. The experimental results show that the hybrid A/D converter operated perfectly and that SNR was 84.8 dB (bit accuracy~13.8 bit) at a band width of 9.1 MHz. This converter has the highest performance of all sigma-delta A/D converters.