Bugs are becoming unavoidable in complex integrated circuit design. It is imperative to identify the bugs as soon as possible through post-silicon debug. For post-silicon debug, observability is one of the biggest challenges. Scan-based debug mechanism provides high observability by reusing scan chains. However, it is not feasible to scan dump cycle-by-cycle during program execution due to the excessive time required. In fact, it is not necessary to scan out the error-free states. In this paper, we introduce Suspect Window to cover the clock cycle in which the bug is triggered. Then, we present an efficient approach to determine the suspect window. Based on Suspect Window, we propose a novel debug mechanism to locate the bug both temporally and spatially. Since scan dumps are only taken in the suspect window with the proposed mechanism, the time required for locating the bug is greatly reduced. The approaches are evaluated using ISCAS'89 and ITC'99 benchmark circuits. The experimental results show that the proposed mechanism can significantly reduce the overall debug time compared to scan-based debug mechanism while keeping high observability.

An improved bidirectional search algorithm for computing the weight spectrum of convolutional codes is presented. This algorithm does not employ the column distance function of a code which plays an important role in the original bidirectional search algorithm. We show the proposed algorithm can reduce computaion time for obtaining the weigth spectrum of convolutional codes significantly compared with that of the bidirectional search algorithm.

In this letter, we propose an improved speech/ nonspeech classification method to effectively classify a multimedia source. To improve performance, we introduce a feature based on spectral duration analysis, and combine recently proposed features such as high zero crossing rate ratio (HZCRR), low short time energy ratio (LSTER), and pitch ratio (PR). According to the results of our experiments on speech, music, and environmental sounds, the proposed method obtained high classification results when compared with conventional approaches.

Thinking process development diagram is a graphical expression from which readers can easily find not only the hierarchy of a given problem but the relationship between the problem and the solution. Although that has been developed as an idea creation support tool in the field of mechanical design, we referred to the restricted version as clamshell diagram to attempt to apply to other fields. In this paper we propose the framework for drawing the diagram of the SQL statement. The basic idea is to supply the hierarchical code fragments of a given SQL statement in the left side of the diagram and to put the meaning written in a natural language in the right. To verify the usefulness of the diagram expression, we actually drew several clamshell diagrams. For three SQL statements that are derived from the same specification, the resulting diagrams enable us to understand the difference visually.

Nonlinear distortions in power amplifiers (PAs) generate spectral regrowth at the output, which causes interference to adjacent channels and errors in digitally modulated signals. This paper presents a novel method to evaluate adjacent channel leakage power ratio (ACPR) and error vector magnitude (EVM) from the amplitude-to-amplitude (AM/AM) and amplitude-to-phase (AM/PM) characteristics. The transmitted signal is considered to be complex Gaussian distributed in orthogonal frequency-division multiplexing (OFDM) systems. We use the Mehler formula to derive closed-form expressions of the PAs output power spectral density (PSD), ACPR and EVM for memoryless PA and memory PA respectively. We inspect the derived relationships using an OFDM signal in the IEEE 802.11a WLAN standard. Simulation results show that the proposed method is appropriate to predict the ACPR and EVM values of the nonlinear PA output in OFDM systems, when the AM/AM and AM/PM characteristics are known.

This paper investigates dynamic spectrum access based on MAC-Layer spectrum sensing and prior channel pre-allocation strategy. We first combine channel utilization with channel state transition probability from idle to busy to reflect the channel opportunity quality in cognitive radio systems. Then a MAC-Layer spectrum sensing algorithm based on Channel Opportunity Quality Descending Order (COQDO) is proposed for the single secondary user scenario, so that the single secondary user can be provided with dynamic spectrum access. For the multi-secondary users scenario, in order to solve the channel collision problem among secondary users in dynamic spectrum access, a joint MAC-Layer spectrum sensing and prior channel pre-allocation algorithm is proposed and analyzed. Channel collision problem occurs when more than one secondary users detect the channel as idle and access it at the same time. Furthermore, the prior channel pre-allocation is optimized by using the conventional Color Sensitive Graph Coloring (CSGC) algorithm. Extensive simulation results are presented to compare our proposed algorithms with existing algorithms in terms of idle channel search delay and accumulated channel handoff delay.

A cooperative relaying system with transmission scheduling is investigated. Cooperative relaying is composed of multiple links because the source sends the data to more than one receiver, and the destination receives multiple data transmitted by more than one transmitter. Therefore, if the source can transmit the data when the channel gains of the links are high, it is not clear which channel gains should be high in order to achieve high spectral efficiency. In the present letter, the spectral efficiency of a cooperative relaying system is theoretically derived under the assumption that the source transmits the data only when the channel gains of links are above certain threshold values. Numerical results reveal that a high spectral efficiency can be achieved by assuring a high channel gain for the link with the highest average received power among links to the destination.

An efficient noise reduction algorithm is proposed to improve speech recognition performance for human machine interfaces. In the algorithm, a probabilistic adaptation mode controller (AMC) is designed and adopted to the generalized sidelobe canceller (GSC). To detect target speech intervals, the proposed AMC calculates the inter-channel correlation and estimates speech absence probability (SAP). Based on the SAP, the adaptation mode of the adaptive filter in the GSC is decided. Experimental results show the proposed algorithm significantly improves speech recognition performances and signal-to-noise ratios in real noisy environments.

A simple suboptimal power allocation method is proposed for SC-FDMA systems. It is known that the performance of constant power-based allocation methods is close to that of optimal solutions. In this letter, by utilizing the waterfilling condition inequality derived for SC-FDMA systems, a threshold is set to select subcarriers for loading constant power to these selected subcarriers. It offers competitive performance as confirmed by the simulation results.

This study investigates a band extension technique for narrow-band telephony speech. The proposed technique employs full wave rectification that nonlinearly generates high-band overtones from the low band. In order to improve the conventional technique, this study investigates a frame-by-frame gain control based on the estimation of gain parameter from narrow-band telephony speech. A subjective evaluation indicates that the proposed technique outperforms the conventional technique.

A technique for reducing the footprints of HMM-based speech synthesis systems by tying all covariance matrices of state distributions is described. HMM-based speech synthesis systems usually leave smaller footprints than unit-selection synthesis systems because they store statistics rather than speech waveforms. However, further reduction is essential to put them on embedded devices, which have limited memory. In accordance with the empirical knowledge that covariance matrices have a smaller impact on the quality of synthesized speech than mean vectors, we propose a technique for clustering mean vectors while tying all covariance matrices. Subjective listening test results showed that the proposed technique can shrink the footprints of an HMM-based speech synthesis system while retaining the quality of the synthesized speech.

Traditional wavelet-based speech enhancement algorithms are ineffective in the presence of highly non-stationary noise because of the difficulties in the accurate estimation of the local noise spectrum. In this paper, a simple method of noise estimation employing the use of a voice activity detector is proposed. We can improve the output of a wavelet-based speech enhancement algorithm in the presence of random noise bursts according to the results of VAD decision. The noisy speech is first preprocessed using bark-scale wavelet packet decomposition ( BSWPD ) to convert a noisy signal into wavelet coefficients (WCs). It is found that the VAD using bark-scale spectral entropy, called as BS-Entropy, parameter is superior to other energy-based approach especially in variable noise-level. The wavelet coefficient threshold (WCT) of each subband is then temporally adjusted according to the result of VAD approach. In a speech-dominated frame, the speech is categorized into either a voiced frame or an unvoiced frame. A voiced frame possesses a strong tone-like spectrum in lower subbands, so that the WCs of lower-band must be reserved. On the contrary, the WCT tends to increase in lower-band if the speech is categorized as unvoiced. In a noise-dominated frame, the background noise can be almost completely removed by increasing the WCT. The objective and subjective experimental results are then used to evaluate the proposed system. The experiments show that this algorithm is valid on various noise conditions, especially for color noise and non-stationary noise conditions.

We analyze the performance of an adaptive communication scheme in which by employing limited feedback, the source will decide to transmit signal to the destination either by the direct link or by the direct and relaying links. Specifically, by using the instantaneous SNR as the metric, if the S-D link is better, the source will transmit to destination directly. Otherwise, the two-phase transmission mode will be triggered in which source cooperates with the relay or transmits twice within two time slots based on the quality of the received signal at the relay. Initially, the spectral efficiency is derived by calculating the probabilities of direct transmission and two-phase transmission mode. Subsequently, the BER performance for the adaptive cooperation schemes is analyzed by considering the BER routines of two events: the source transmits the signal alone or cooperates with the relay. Also, the optimum power allocation is studied based on the BER result. Finally, Monte-Carlo simulation results are presented to confirm the performance enhancement offered by the proposed scheme.

To improve speech recognition performance, acoustic feature transformation based on discriminant analysis has been widely used. For the same purpose, discriminative training of HMMs has also been used. In this letter we investigate the effectiveness of these two techniques and their combination. We also investigate the robustness of matched and mismatched noise conditions between training and evaluation environments.

We propose a surface profiling algorithm by white-light interferometry that extends sampling interval to twice of the widest interval among those used in conventional algorithms. The proposed algorithm uses a novel function called an in-phase component of an interferogram to detect the peak of the interferogram, while conventional algorithms used the squared-envelope function or the envelope function. We show that the in-phase component has the same peak as the corresponding interferogram when an optical filter has a symmetric spectral distribution. We further show that the in-phase component can be reconstructed from sampled values of the interferogram using the so-called quadrature sampling technique. Since reconstruction formulas used in the algorithm are very simple, the proposed algorithm requires low computational costs. Simulation results show the effectiveness of the proposed algorithm.

Substrate noise analysis has become increasingly important in recent LSI design. This is because substrate noise, which affects PLLs, causes jitter that results in timing error. Conventional analysis techniques of substrate noise are, however, impractical for large-scale designs that have hundreds of millions of transistors because the computational complexity is too huge. To solve this problem, we have developed a fast substrate noise analysis technique for large-scale designs, in which a chip is divided into multiple domains and the circuits of each domain are reduced into a macro model. Using this technique, we have designed a processor chip for use in the supercomputer (die size: 20 mm 21 mm, frequency: 3.2 GHz, transistor count: 350M). Computation time with this design is five times faster than that with a 1/3000 scale design using a conventional technique, while resulting discrepancy with measured period jitter is less than 15%.

The selection of effective features is especially important in achieving highly accurate speech recognition. Although the mel-cepstrum is a popular and effective feature for speech recognition, it is still unclear that the filterbank adopted in the mel-cepstrum always produces the optimal performance regardless of the phonetic environment of any specific speech recognition task. In this paper, we propose a new cepstral domain feature extraction approach utilizing the entropic distance-based filterbank for highly accurate speech recognition. Experimental results showed that the cepstral features employing the proposed filterbank reduce the relative error by 31% for clean as well as noisy speech compared to the mel-cepstral features.

In this paper, a fast and memory-efficient VLSI architecture for output probability computations of continuous Hidden Markov Models (HMMs) is presented. These computations are the most time-consuming part of HMM-based recognition systems. High-speed VLSI architectures with small registers and low-power dissipation are required for the development of mobile embedded systems with capable human interfaces. We demonstrate store-based block parallel processing (StoreBPP) for output probability computations and present a VLSI architecture that supports it. When the number of HMM states is adequate for accurate recognition, compared with conventional stream-based block parallel processing (StreamBPP) architectures, the proposed architecture requires fewer registers and processing elements and less processing time. The processing elements used in the StreamBPP architecture are identical to those used in the StoreBPP architecture. From a VLSI architectural viewpoint, a comparison shows the efficiency of the proposed architecture through efficient use of registers for storing input feature vectors and intermediate results during computation.

This paper proposes a hardware-based parallel pattern matching engine using a memory-based bit-split string matcher architecture. The proposed bit-split string matcher separates the transition table from the state table, so that state transitions towards the initial state are not stored. Therefore, total memory requirements can be minimized.

A scalable speech codec consisting of a harmonic codec as the core layer and MDCT-based transform codec as the enhancement layer is often required to provide both very low-rate core communication and fine granular scalability. This structure, however, has a serious drawback for practical use because a time delay caused by transform in each layer is accumulated, resulting in a long overall codec delay. In this letter, a new MDCT structure is proposed to reduce the overall codec delay by eliminating the accumulation of time delay by each transform. In the proposed structure, the time delay is first reduced by forcing two transforms to share a common look-ahead. The error components of MDCT caused by the look-ahead sharing are then analyzed and compensated in the decoder, resulting in perfect reconstruction. The proposed structure reduces the codec delay by the frame size, with an equivalent coding efficiency.