The radiation properties of oscillating electric dipoles are studied theoretically for three and four layered systems including a single metallic slab based on angular spectrum representation of vector spherical waves. One of the remarkable results obtained is the transmission energy spectrum showing strong dependence on the thickness of a dielectric layer placed between oscillating electric dipole and metallic surface, which explains the experimental results of molecular fluorescence into surface plasmon modes. The theory based on angular spectrum representation and tunneling current provides us with a clear identification of plasmonic excitation transfer, transmission loss associated with plasmon transport in metallic layer, and energy dissipation or quenching of excitation due to surface plasmon excitation at the metallic surface in relation to the characteristic complex wave number of evanescent waves.

Since WLAN (wireless LAN) systems share the 2.4-GHz frequency band with microwave ovens, interference caused by radiated oven noise is a serious problem in practical WLAN application. To mitigate the oven noise interference in DS-SS (direct-sequence spread spectrum) WLAN systems, the use of adaptive filters is proposed. This method is based on the fact that oven noise behaves like CW (continuous wave) interference within a short duration. In contrast to previous reduction techniques for oven noise, this method can be implemented without changing any specifications of current WLAN systems. The results of numerical and experimental analyses clearly demonstrate the effectiveness of adaptive filters for improving the bit error rates of WLAN links subject to oven noise interference.

The motion of arc spots of breaking arc is investigated for Ag electrical contacts in DC 42 V/10 A resistive circuit using a high-speed camera. Also, the eroded contact surfaces are observed with a microscope after each breaking operation. As results, some kinds of different films and eroded regions are distinguished. Diameters of these regions are corresponding to the widths of the cathode and anode spot regions that are obtained by using the high-speed camera. It is found that the films and eroded regions on the electrical contacts are generated at different stages of the breaking arc.

In each contact material (Ag, Cu, Ni, and Fe), the breaking arcs occurring between an electrical contact pair in a resistive circuit of DC42 V/10.5 A were observed with a high-speed camera (1000 frames/s). Arc voltage and arc current were also measured simultaneously. By analyzing cathode and anode bright spots in the photographs, the positions of cathode and anode bright spots on contact surfaces were plotted on the graph. As a result, cathode and anode bright spots were found to express the characteristic motion in each material. Moreover, by comparing those results with the photograph of contact surface after all operations.

This paper proposes an adaptive noise canceller (ANC) with low signal-distortion for human-robot communication. The proposed ANC has two sets of adaptive filters for noise and crosstalk; namely, main filters (MFs) and subfilters (SFs) connected in parallel thereto. To reduce signal-distortion in the output, the stepsizes for coefficient adaptation in the MFs are controlled according to estimated signal-to-noise ratios (SNRs) of the input signals. This SNR estimation is carried out using SF output signals. The stepsizes in the SFs are determined based on the ratio of the primary and the reference input signals to cope with a wider range of SNRs. This ratio is used as a rough estimate of the input signal SNR at the primary input. Computer simulation results using TV sound and human voice recorded in a carpeted room show that the proposed ANC reduces both residual noise and signal-distortion by as much as 20 dB compared to the conventional ANC. Evaluation in speech recognition with this ANC reveals that with a realistic TV sound level, as good recognition rate as in the noise-free condition is achieved.

The performance of integrated segmentation and recognition of handwritten numeral strings relies on the classification accuracy and the non-character resistance of the underlying character classifier, which is variable depending on the techniques of pattern normalization, feature extraction, and classifier structure. In this paper, we evaluate the effects of 12 normalization functions and four selected feature types on numeral string recognition. Slant correction (deslant) is combined with the normalization functions and features so as to create 96 feature vectors, which are classified using two classifier structures. In experiments on numeral string images of the NIST Special Database 19, the classifiers have yielded very high string recognition accuracies. We show the superiority of moment normalization with adaptive aspect ratio mapping and the gradient direction feature, and observed that slant correction is beneficial to string recognition when combined with good normalization methods.

The measured values of electromagnetic disturbances should strongly correlate with degradation in the communication quality of digital wireless communication systems. The Amplitude Probability Distribution (APD) of a disturbance represents statistical information as applicable measurement readings that meet the above requirement. In this paper, correlations between APD measurements of disturbances and the bit error rate (BER) as a quality degradation index for victim systems are quantitatively investigated. Disturbance regulation by APD measurements is discussed from the viewpoint of protecting systems from disturbances. This investigation specifically considers the situation in which a repetition pulse disturbance impacts PHS and W-CDMA systems assumed as victims. The results confirm high correlations between the APD and BER not only experimentally but also theoretically under some conditions. A disturbance regulation criterion based on APD measurements is thus proposed for compliance testing of electronic appliances with the potential to act as disturbance noise sources.

Recently, DOAS (differential optical absorption spectroscopy) has been used for nondestructive air monitoring, in which the LS (least squares) method is used to calculate trace gas concentrations due to its computational simplicity. This paper applies the ICA (independent component analysis) method to the DOAS system of air monitoring, since the LS method is insufficient to recover the desired spectra perfectly due to sparsity characteristic. If the sparsity of reference spectra in the DOAS system imposes the assumption of independence, the ICA algorithm can be used. The proposed method is used to regress the observed spectrum on the estimates of the reference spectra. The ICA algorithm can be seen as a preprocessing method where the ICs of the references are used as the input in the regression. The performance of the proposed method is evaluated in simulation studies using synthetic data.

Recently, automatic speech recognition in a car has practical uses for applications like car-navigation and hands-free telephone dialers. For noise robustness, the current successes are based on the assumption that there is only a stationary cruising noise. Therefore, the recognition rate is greatly reduced when there is music or news coming from a radio or a CD player in the car. Since reference signals are available from such in-vehicle units, there is great hope that echo cancellers can eliminate the echo component in the observed noisy signals. However, previous research reported that the performance of an echo canceller is degraded in very noisy conditions. This implies it is desirable to combine the processes of echo cancellation and noise reduction. In this paper, we propose a system that uses echo cancellation and spectral subtraction simultaneously. A stationary noise component for spectral subtraction is estimated through the adaptation of an echo canceller. In our experiments, this system significantly reduced the errors in automatic speech recognition compared with the conventional combination of echo cancellation and spectral subtraction.

Jacobian Adaptation (JA) has been successfully used in Automatic Speech Recognition (ASR) systems to adapt the acoustic models from the training to the testing noise conditions. In this work we present an improvement of JA for speaker verification, where a specific training noise reference is estimated for each speaker model. The new proposal, which will be referred to as Model-dependent Noise Reference Jacobian Adaptation (MNRJA), has consistently outperformed JA in our speaker verification experiments.

In this paper, we address issues in improving hands-free speech recognition performance in different car environments using multiple spatially distributed microphones. In the previous work, we proposed the multiple linear regression of the log spectra (MRLS) for estimating the log spectra of speech at a close-talking microphone. In this paper, the concept is extended to nonlinear regressions. Regressions in the cepstrum domain are also investigated. An effective algorithm is developed to adapt the regression weights automatically to different noise environments. Compared to the nearest distant microphone and adaptive beamformer (Generalized Sidelobe Canceller), the proposed adaptive nonlinear regression approach shows an advantage in the average relative word error rate (WER) reductions of 58.5% and 10.3%, respectively, for isolated word recognition under 15 real car environments.

This paper presents a study on the blind separation of a convoluted mixture of speech signals using Frequency Domain Independent Component Analysis (FDICA) algorithm based on the negentropy maximization of Time Frequency Series of Speech (TFSS). The comparative studies on the negentropy approximation of TFSS using generalized Higher Order Statistics (HOS) of different nonquadratic, nonlinear functions are presented. A new nonlinear function based on the statistical modeling of TFSS by exponential power functions has also been proposed. The estimation of standard error and bias, obtained using the sequential delete-one jackknifing method, in the approximation of negentropy of TFSS by different nonlinear functions along with their signal separation performance indicate the superlative power of the exponential-power-based nonlinear function. The proposed nonlinear function has been found to speed-up convergence with slight improvement in the separation quality under reverberant conditions.

A millimeter wave BPF constructed from the WG mode dielectric disk resonators is presented. The design chart for the high Q WG mode resonator is obtained from Qu calculation of some WG modes. By using the design chart, high Q WG mode resonator having no influence of unwanted higher order resonances is designed. Designed resonators have different diameter and various Resonance Frequency Separation respectively. A 3 stage maximally flat BPF is constructed so that each resonator may be coupled laterally on the edge of the disk. Designed center frequency is 62.47 GHz and 3 dB bandwidth is 100 MHz. As a result, this BPF has insertion loss of 1.5 dB and some spurious responses which were existed conventional WG mode BPF are reduced considerably.

A speech signal captured by a distant microphone is generally smeared by reverberation, which severely degrades both the speech intelligibility and Automatic Speech Recognition (ASR) performance. Previously, we proposed a single-microphone dereverberation method, named "Harmonicity based dEReverBeration (HERB)." HERB estimates the inverse filter for an unknown room transfer function by utilizing an essential feature of speech, namely harmonic structure. In previous studies, improvements in speech intelligibility was shown solely with spectrograms, and improvements in ASR performance were simply confirmed by matched condition acoustic model. In this paper, we undertook a further investigation of HERB's potential as regards to the above two factors. First, we examined speech intelligibility by means of objective indices. As a result, we found that HERB is capable of improving the speech intelligibility to approximately that of clean speech. Second, since HERB alone could not improve the ASR performance sufficiently, we further analyzed the HERB mechanism with a view to achieving further improvements. Taking the analysis results into account, we proposed an appropriate ASR configuration and conducted experiments. Experimental results confirmed that, if HERB is used with an ASR adaptation scheme such as MLLR and a multicondition acoustic model, it is very effective for improving ASR performance even in unknown severely reverberant environments.

This paper proposes a new class of polyphase ZCZ (zero-correlation zone) sequence sets which satisfy a mathematical upper bound. The proposed ZCZ sequence sets are obtained from DFT matrices and unitary matrices. In addition, this paper discusses the cross-correlation property between different ZCZ sequence sets which belong to the proposed class.

An alaryngeal speech enhancement system is proposed to improve the intelligibility and quality of speech signals generated by an artificial larynx transducer (ALT). Proposed system identifies the voiced segments of alaryngeal speech signal, by using pattern recognition methods, and replaces these by their equivalent voiced segments of normal speech. Evaluation results show that proposed system provides a fairly good improvement of the quality and intelligibility of ALT generated speech.

Severe frequency-selective fading, encountered in a broadband wireless mobile communication, significantly degrades the bit error rate (BER) performance of direct sequence spread spectrum (DSSS) signal transmission with rake combining. In this paper, frequency-domain pre-equalization transmission, called pre-FDE transmission, is presented for orthogonal multicode DSSS signal transmission. It is confirmed by the computer simulation that pre-FDE transmission can achieve a BER performance almost identical to that attainable by FDE reception.

A robust microphone array for speech enhancement and noise suppression is studied in this paper. To overcome target signal cancellation problem of conventional beamformer caused by array imperfections or reverberation effects of acoustic enclosure, the proposed microphone array adopts an arbitrary model of channel transfer function (TF) relating microphone and speech source. Since the estimation of channel TF itself is often intractable, herein, transfer function ratio (TFR) is estimated instead and used to form a suboptimal beamformer. A robust TFR estimation method is proposed based on signal subspace analysis technique against stationary or slowly varying noise. Experiments using simulated signal and actual signal recorded in a real room illustrate that the proposed method has high performance in adverse environment.

The main contribution of this work is to present several hardware implementations of an "n choose k" counter (C(n,k) counter for short), which lists all n-bit numbers with (n-k) 0's and k 1's, and to show their applications. We first present concepts of C(n,k) counters and their efficient implementations on an FPGA. We then go on to evaluate their performance in terms of the number of used slices and the clock frequency for the Xilinx VirtexII family FPGA XC2V3000-4. As one of the real life applications, we use a C(n,k) counter to accelerate a digital halftoning method that generates a binary image reproducing an original gray-scale image. This method repeatedly replaces an image pattern in small square regions of a binary image by the best one. By the partial exhaustive search using a C(n,k) counter we succeeded in accelerating the task of finding the best image pattern and achieved a speedup factor of more than 2.5 over the simple exhaustive search.

This paper focuses on the underdetermined blind source separation (BSS) of three speech signals mixed in a real environment from measurements provided by two sensors. To date, solutions to the underdetermined BSS problem have mainly been based on the assumption that the speech signals are sufficiently sparse. They involve designing binary masks that extract signals at time-frequency points where only one signal was assumed to exist. The major issue encountered in previous work relates to the occurrence of distortion, which affects a separated signal with loud musical noise. To overcome this problem, we propose combining sparseness with the use of an estimated mixing matrix. First, we use a geometrical approach to detect when only one source is active and to perform a preliminary separation with a time-frequency mask. This information is then used to estimate the mixing matrix, which allows us to improve our separation. Experimental results show that this combination of time-frequency mask and mixing matrix estimation provides separated signals of better quality (less distortion, less musical noise) than those extracted without using the estimated mixing matrix in reverberant conditions where the reverberant time (TR) was 130 ms and 200 ms. Furthermore, informal listening tests clearly show that musical noise is deeply lowered by the proposed method comparatively to the classical approaches.