In this letter, we propose a new type of recursive least squares (RLS) algorithms without using the initial information of a parameter or a state to be estimated. The proposed RLS algorithm is first obtained for a generic linear model and is then extended to a state estimator for a stochastic state-space model. Compared with the existing algorithms, the proposed RLS algorithms are simpler and more numerically stable. It is shown through simulation that the proposed RLS algorithms have better numerical stability for digital computations than existing algorithms.

In this comment we point out that the mapping from carry-propagation adders to carry-save adders in the context of shift-and-add multiplication is inconsistent. Based on this it is shown that the implementation in Ref.[1] does not achieve any complexity reduction in practice.

This paper presents methods for detecting transistor short faults using logic level fault simulation and test generation. The paper considers two types of transistor level faults, namely strong shorts and weak shorts, which were introduced in our previous research. These faults are defined based on the values of outputs of faulty gates. The proposed fault simulation and test generation are performed using gate-level tools designed to deal with stuck-at faults, and no transistor-level tools are required. In the test generation process, a circuit is modified by inserting inverters, and a stuck-at test generator is used. The modification of a circuit does not mean a design-for-testability technique, as the modified circuit is used only during the test generation process. Further, generated test patterns are compacted by fault simulation. Also, since the weak short model involves uncertainty in its behavior, we define fault coverage and fault efficiency in three different way, namely, optimistic, pessimistic and probabilistic and assess them. Finally, experimental results for ISCAS benchmark circuits are used to demonstrate the effectiveness of the proposed methods.

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.

This paper addresses the problem of voice activity detection (VAD) in noisy environments. The VAD method proposed in this paper is based on a statistical model approach, and estimates statistical models sequentially without a priori knowledge of noise. Namely, the proposed method constructs a clean speech / silence state transition model beforehand, and sequentially adapts the model to the noisy environment by using a switching Kalman filter when a signal is observed. In this paper, we carried out two evaluations. In the first, we observed that the proposed method significantly outperforms conventional methods as regards voice activity detection accuracy in simulated noise environments. Second, we evaluated the proposed method on a VAD evaluation framework, CENSREC-1-C. The evaluation results revealed that the proposed method significantly outperforms the baseline results of CENSREC-1-C as regards VAD accuracy in real environments. In addition, we confirmed that the proposed method helps to improve the accuracy of concatenated speech recognition in real environments.

In a power grid used to distribute electricity, optical fibers can be inserted inside overhead ground wires to form an optical network infrastructure for data communications. Dense wavelength division multiplexing (DWDM)-based optical networks present a promising approach to achieve a scalable backbone network for power grids. This paper proposes a complete optimization procedure for optical network designs based on an existing power grid. We design a network as a subgraph of the power grid and divide the network topology into two layers: backbone and access networks. The design procedure includes physical topology design, routing and wavelength assignment (RWA) and optical amplifier placement. We formulate the problem of topology design into two steps: selecting the concentrator nodes and their node members, and finding the connections among concentrators subject to the two-connectivity constraint on the backbone topology. Selection and connection of concentrators are done using integer linear programming (ILP). For RWA and optical amplifier placement problem, we solve these two problems together since they are closely related. Since the ILP for solving these two problems becomes intractable with increasing network size, we propose a simulated annealing approach. We choose a neighborhood structure based on path-switching operations using k shortest paths for each source and destination pair. The optimal number of optical amplifiers is solved based on local search among these neighbors. We solve and present some numerical results for several randomly generated power grid topologies.

In optical burst switching (OBS) networks, burst with different numbers of hops experience unfairness in terms of the burst loss probability. In this paper, we propose a preemptive scheme based on the number of transit hops in OBS networks. In our proposed scheme, preemption is performed with two thresholds; one is for the total number of hops of a burst and the other is for the number of transit hops the burst has passed through. We evaluate the performance of the scheme by simulation, and numerical examples show that the proposed scheme improves the fairness among the bursts with different numbers of hops, keeping the overall burst loss probability the same as that for the conventional OBS transmission without preemption.

Test compression / decompression scheme for reducing the test application time and memory requirement of an LSI tester has been proposed. In the scheme, the employed coding algorithms are tailored to a given test data, so that the tailored coding algorithm can highly compress the test data. However, these methods have some drawbacks, e.g., the coding algorithm is ineffective in extra test data except for the given test data. In this paper, we introduce an embedded decompressor that is reconfigurable according to coding algorithms and given test data. Its reconfigurability can overcome the drawbacks of conventional decompressors with keeping high compression ratio. Moreover, we propose an architecture of reconfigurable decompressors for four variable-length codings. In the proposed architecture, the common functions for four codings are implemented as fixed (or non-reconfigurable) components so as to reduce the configuration data, which is stored on an ATE and sent to a CUT. Experimental results show that (1) the configuration data size becomes reasonably small by reducing the configuration part of the decompressor, (2) the reconfigurable decompressor is effective for SoC testing in respect of the test data size, and (3) it can achieve an optimal compression of test data by Huffman coding.

This letter presents a race-free mixed serial-parallel comparison (RFMSPC) scheme which uses both serial and parallel CAMs in a match line. A self-reset search line scheme for the serial CAM is proposed to avoid the timing race problem and additional timing penalties. Various 32 entry CAMs are designed using 90 nm 1.2 V CMOS process to verify the proposed RFMSPC scheme. It shows that the RFMSPC saves power consumption by 40%, 53% and 63% at the cost of a 4%, 6% and 16% increase in search time according to 1, 2, and 4 serial CAM bits in a match line.

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.

The MOS switch with bootstrapped technique is widely used in low-voltage switched-capacitor circuit. The switched-capacitor circuit with the bootstrapped technique could be a dangerous design approach in the nano-scale CMOS process due to the gate-oxide transient overstress. The impact of gate-oxide transient overstress on MOS switch in switched-capacitor circuit is investigated in this work with the sample-and-hold amplifier (SHA) in a 130-nm CMOS process. After overstress on the MOS switch of SHA with unity-gain buffer, the circuit performances in time domain and frequency domain are measured to verify the impact of gate-oxide reliability on circuit performances. The oxide breakdown on switch device degrades the circuit performance of bootstrapped switch technique.

We consider a data set in which each example is an n-dimensional Boolean vector labeled as true or false. A pattern is a co-occurrence of a particular value combination of a given subset of the variables. If a pattern appears frequently in the true examples and infrequently in the false examples, we consider it a good pattern. In this paper, we discuss the problem of determining the data size needed for removing "deceptive" good patterns; in a data set of a small size, many good patterns may appear superficially, simply by chance, independently of the underlying structure. Our hypothesis is that, in order to remove such deceptive good patterns, the data set should contain a greater number of examples than that at which a random data set contains few good patterns. We justify this hypothesis by computational studies. We also derive a theoretical upper bound on the needed data size in view of our hypothesis.

This paper presents a target location estimation method that can use a pair of leaky coaxial cables to determine the 2D coordinates of the target. Since convention location techniques using leaky coaxial cables can find a target location along the cable in 1D, they have been unable to locate it in 2D planes. The proposed method enables us to estimate target on a 2D plane using; 1) a beam-forming technique and 2) a reconstruction technique based on Hough transform. Leaky coaxial cables are equipped with numerous slots at regular interval, which can be utilized as antenna arrays that acts both as transmitters and receivers. By completely exploiting this specific characteristic of leaky coaxial cables, we carried out an antenna array analysis that performs in a beam-forming fashion. Simulation and experimental results support the effectiveness of the proposed target location method.

In this paper, we propose a novel sound field reproduction system that uses the simultaneous perturbation (SP) method as well as two fast convergence techniques. Sound field reproduction systems that reproduce any desired signal at listener's ear generally use fixed preprocessing filters that are determined by the transfer functions from loudspeakers to control points in advance. However, control point movement results in severe localization errors. Our solution is a sound field reproduction system, based on the SP method, which uses only an error signal to update the filter coefficients. The SP method can track all control point movements but suffers from slow convergence. Hence, we also propose two methods that offer improved convergence speeds. One is a delay control method that compensates the delay caused by back-and-forth control point movements. The other is a compensation method that offsets the localization error caused by head rotation. Simulations demonstrate that the proposed methods can well track control point movements while offering reasonable convergence speeds.

In this paper, a fast mode decision method for intra-prediction is proposed to reduce the computational complexity of H.264/AVC encoders. With edge information, we propose a novel fast estimation algorithm to reduce the computation overhead of H.264/AVC for mode selection, where the edge direction of each coding block is detected from only part of the transformed coefficients. Hence, the computation complexity is greatly reduced. Experimental results show that the proposed fast mode decision method can eliminate about 81.34% encoding time for all intra-frame sequences with acceptable degradation of averaged PSNR and bitrates.

This paper proposes a new algorithm named Adaptive Running Spectrum Filtering (ARSF) to restore the amplitude spectra of speech corrupted by additive noises. Based on the pre-hand noise estimation, adaptive filtering is used in speech modulation spectra according to the noise conditions. The periodic structures in the amplitude spectra are kept against noise distortion. Since the amplitude spectral structures contain the information of fundamental frequency, which is the inverse of pitch period, ARSF algorithm is added into robust pitch detection to increase the accuracy. Compared with the conventional methods, experimental results show that the proposed method significantly improves the robustness of pitch detection against noise conditions with several types and SNRs.

In forensic voice telephony speaker verification, we may be requested to identify a speaker in a very noisy environment, unlike the conditions in general research. In a noisy environment, we process speech first by clarifying it. However, the previous study of speaker verification from clarified speech did not yield satisfactory results. In this study, we experimented on speaker verification with clarification of speech in a noisy environment, and we examined the relationship between improving acoustic quality and speaker verification results. Moreover, experiments with realistic noise such as a crime prevention alarm and power supply noise was conducted, and speaker verification accuracy in a realistic environment was examined. We confirmed the validity of speaker verification with clarification of speech in a realistic noisy environment.

Per-test diagnosis based on the X-fault model is an effective approach for a circuit with physical defects of non-deterministic logic behavior. However, the extensive use of vias and buffers in a deep-submicron circuit and the unpredictable order relation among threshold voltages at the fanout branches of a gate have not been fully addressed by conventional per-test X-fault diagnosis. To take these factors into consideration, this paper proposes an improved per-test X-fault diagnosis method, featuring (1) an extended X-fault model to handle vias and buffers and (2) the use of occurrence probabilities of logic behaviors for a physical defect to handle the unpredictable relation among threshold voltages. Experimental results show the effectiveness of the proposed method.

Image segmentation is an essential processing step for many image analysis applications. In this paper, a novel image segmentation algorithm using fuzzy C-means clustering (FCM) with spatial constraints based on Markov random field (MRF) via Bayesian theory is proposed. Due to disregard of spatial constraint information, the FCM algorithm fails to segment images corrupted by noise. In order to improve the robustness of FCM to noise, a powerful model for the membership functions that incorporates local correlation is given by MRF defined through a Gibbs function. Then spatial information is incorporated into the FCM by Bayesian theory. Therefore, the proposed algorithm has both the advantages of the FCM and MRF, and is robust to noise. Experimental results on the synthetic and real-world images are given to demonstrate the robustness and validity of the proposed algorithm.