Micro-gap electrostatic discharge (ESD) events due to a human with charge voltages below 1000 V cause serious malfunctions in high-tech information devices. For clarifying such a mechanism, it is indispensable to grasp the spark process of such micro-gap ESDs. For this purpose, two types of spark-resistance laws proposed by Rompe-Weizel and Toepler have often been used, which were derived from the hypotheses that spark conductivity be proportional to the internal energies and charges injected into a spark channel, respectively. However, their validity has not well been verified. To examine which spark-resistance formula could be applied for micro-gap ESDs, with a 12-GHz digital oscilloscope, we previously measured the discharge currents through the hand-held metal piece from a charged human with respect to charged voltages of 200 V and 2000 V, and thereby derived the conductance of a spark gap to reveal that both of their hypotheses are roughly valid in the initial stage of sparks. In this study, to further verify the above spark hypotheses, we derived the discharge voltages in closed forms across a spark gap based on the above spark-resistance formulae, and investigated which spark-resistance formula could be applied for micro-gap ESDs in comparison of spark gaps estimated from the measured discharge currents. As a result, we found that Rompe-Weizel's formula could well explain spark properties for micro-gap ESDs than Toepler's one regardless of charge voltages and approach speeds.

Multi-dimensional (MD) periodic complementary array sets (CASs) with impulse-like MD periodic autocorrelation function are naturally generalized to (one dimensional) periodic complementary sequence sets, and such array sets are widely applied to communication, radar, sonar, coded aperture imaging, and so forth. In this letter, based on multi-dimensional perfect arrays (MD PAs), a method for constructing MD periodic CASs is presented, which is carried out by sampling MD PAs. It is particularly worth mentioning that the numbers and sizes of sub-arrays in the proposed MD periodic CASs can be freely changed within the range of possibilities. In particular, for arbitrarily given positive integers M and L, two-dimensional periodic polyphase CASs with the number M2 and size L L of sub-arrays can be produced by the proposed method. And analogously, pseudo-random MD periodic CASs can be given when pseudo-random MD arrays are sampled. Finally, the proposed method's validity is made sure by a given example.

Post-wall waveguide slot arrays are potential candidates for millimeter-wave systems. The modeling of the post-walls by the equivalent solid-walls in terms of guided wavelength is indispensable for intensive optimization of slot design for reducing computational load. In the single mode waveguide slot arrays, the modeling errors of the post-wall waveguide by the solid-wall waveguide are serious especially for the transversely located slots. The S-parameter prediction errors become larger as we increase the height of the waveguide to utilize the low-loss advantage of the waveguide. The authors propose a novel post-wall waveguide structure, named as a connected post-wall (C-PW), to enhance the equivalence. The C-PW waveguide keeps enhanced equivalence to the solid-walls even for a larger substrate height. The predictions are confirmed by simulations and measurements. An 8-element linear array of reflection-cancelling slot pairs is designed by using the equivalent solid-wall model to demonstrate the feasibility of the simple design in the C-PW.

A novel approach is proposed for miniaturizing the unit cell size of electromagnetic bandgap (EBG) structures that suppress power plane noise. In this approach, open stubs are introduced into the shunt circuits of these EBG structures. Since the stub length determines the resonant frequencies of the shunt circuit, the proposed structures can maintain the bandgaps at lower frequencies without increasing the unit cell size. The bandgap frequencies were estimated by dispersion analysis based on the Bloch theorem and full-wave simulations. Sample boards of the proposed EBG structures were fabricated with a unit cell size of 2.1 mm. Highly suppressed noise propagation over the estimated frequency range of 1.9-3.6 GHz including the 2.4-GHz wireless-LAN band was experimentally demonstrated.

In this letter, a derivative constraint minimum output energy (MOE) receiver is proposed the offers enhanced robustness against carrier frequency offset (CFO). A theoretical analysis of the output signal-to-interference-plus-noise ratio (SINR) is presented to confirm its efficacy. Numerical results demonstrate that the proposed receiver basically offers the same performance as an optimal receiver with no CFO present.

This paper treats a band-broadening design technique of a dual-band branch-line coupler with matching networks composed of an impedance step and a short-circuited stub based on the equivalent admittance approach. By replacing each right-handed transmission line (RH-TL) with a composite right/left-handed transmission line (CRLH-TL), very flat couplings over a relative bandwidth of about 10% can be obtained at two arbitrary operating frequencies in comparison with previous CRLH-TLs branch-line couplers. Furthermore, by adding periodical open-circuited stubs into RH-TLs of the designed CRLH-TLs branch-line coupler with matching networks, the entire size of the coupler can be reduced to about 50%. Verification of these band-broadening and size-reduction design techniques can be also shown by an electromagnetic simulation and experiment.

Orthogonal frequency division multiplexing (OFDM) signals have high peak-to-average power ratio (PAPR) and cause large nonlinear distortions in power amplifiers (PAs). Memory effects in PAs also become no longer ignorable for the wide bandwidth of OFDM signals. Digital baseband predistorter is a highly efficient technique to compensate the nonlinear distortions. But it usually has many parameters and takes long time to converge. This paper presents a novel predistorter design using a set of orthogonal polynomials to increase the convergence speed and the compensation quality. Because OFDM signals are approximately complex Gaussian distributed, the complex Hermite polynomials which have a closed-form expression can be used as a set of orthogonal polynomials for OFDM signals. A differential envelope model is adopted in the predistorter design to compensate nonlinear PAs with memory effects. This model is superior to other predistorter models in parameter number to calculate. We inspect the proposed predistorter performance by using an OFDM signal referred to the IEEE 802.11a WLAN standard. Simulation results show that the proposed predistorter is efficient in compensating memory PAs. It is also demonstrated that the proposal acquires a faster convergence speed and a better compensation effect than conventional predistorters.

Error-propagation is an important issue and should be carefully coped with in the decision-feedback equalizers (DFE). Ignoring the impact of error-propagation often leads to impractical laboratory results. In this paper, we investigate two novel layered space-frequency equalizers (LSFE) for single-carrier multiple-input multiple-output (MIMO) systems, where the recently proposed frequency-domain equalizer with time domain noise-predictor (FDE-NP) is adopted at each stage of the LSFE. We first derive the partially-connected LSFE with noise predictor (PC-LSFE-NP) which has exactly the same mean square error (MSE) as the conventional LSFE under the assumption of perfect feedback. However, if error-propagation is considered, the proposed PC-LSFE-NP can achieve better performance than the conventional LSFE due to the more reliable feedback output by the decoders. To reduce the interference from the not yet detected layers in the feedback section, we then introduce the fully-connected LSFE with noise predictor (FC-LSFE-NP), in which all layers are implicitly equalized within each stage and their decisions fed back internally. The powerful feedback filter of FC-LSFE-NP brings significant performance superiority over the conventional LSFE and PC-LSFE-NP with either perfect or imperfect feedback. Moreover, we propose a simple soft-demapper for the equalizers to avoid information loss during decoding, and thus, further improve the performance. Finally, we compare the performance of (PC/FC)-LSFE-NP with the existing schemes by computer simulations.

The use of image contrast enhancement has become increasingly essential due to the need to better show the visual information contained within the image for all vision-based systems. This has lead to motivation for the design of a powerful and accurate automatic contrast enhancement for a digital image. Histogram equalization is the most commonly used contrast enhancement method. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method using the automatic histogram separation along with the piecewise transformed function. The contrast enhancement results of the proposed method were not only analyzed through qualitative visual inspection and for quantitative accuracy, but are also compared to the results of other state-of-the-art methods.

At some point in a digital communications receiver, the received analog signal must be sampled. Good performance requires that these samples be taken at the right times. The process of synchronizing the sampler with the received analog waveform is known as timing recovery. Conventional timing recovery techniques perform well only when operating at high signal-to-noise ratio (SNR). Nonetheless, iterative error-control codes allow reliable communication at very low SNR, where conventional techniques fail. This paper provides a detailed review on the timing recovery strategies based on per-survivor processing (PSP) that are capable of working at low SNR. We also investigate their performance in magnetic recording systems because magnetic recording is a primary method of storage for a variety of applications, including desktop, mobile, and server systems. Results indicate that the timing recovery strategies based on PSP perform better than the conventional ones and are thus worth being employed in magnetic recording systems.

We have so far proposed a speaking-aid system for laryngectomees using a statistical voice conversion technique. In the proposed system, artificial speech articulated with extremely small sound source signals is detected with a Non-Audible Murmur (NAM) microphone, and then, the detected artificial speech is converted into more natural voice in a probabilistic manner. Although this system basically allows laryngectomees to speak while keeping the external source signals silent, it is still questionable how much these new sound source signals affect the converted speech quality. In this paper, we investigate the impact of various sound source signals on voice conversion accuracy. Various small sound source signals are designed by changing the spectral envelope and the waveform power independently. We conduct objective and subjective evaluations. The results of these experimental evaluations demonstrate that voice conversion accepts 1) various sound source signals with different spectral envelopes and 2) large degree of power of the sound source signals unless the power of speaking parts is almost equal to that of silence parts. Moreover, we also investigate the effectiveness of enhancing auditory feedback during speaking with the extremely small sound source signals.

Recently, a graph labeling technique based on prime numbers has been suggested for reducing the costly transitive closure computations in RDF query languages. The suggested prime number graph labeling provides the benefit of fast query processing by a simple divisibility test of labels. However, it has an inherent problem that originates with the nature of prime numbers. Since each prime number must be used exclusively, labels can become significantly large. Therefore, in this paper, we introduce a novel optimization technique to effectively reduce the problem of label overflow. The suggested idea is based on graph decomposition. When label overflow occurs, the full graph is divided into several sub-graphs, and nodes in each sub-graph are separately labeled. Through experiments, we also analyze the effectiveness of the graph decomposition optimization, which is evaluated by the number of divisions.

This paper presents an approach to implementing simulation models for SAM fuzzy controllers without the use of external components. The approach represents a fuzzy controller as a composition of simple simulation models which involve only basic operations.

This paper applies the Evolutionary Programming (EP) algorithm and a risk assessment technique to obtain an optimal solution to the Unit Maintenance Scheduling Decision (UMSD) problem subject to economic cost and power security constraints. The proposed approach employs a risk assessment model to evaluate the security of the power supply system and uses the EP algorithm to establish the optimal unit maintenance schedule. The effectiveness of the proposed methodology is verified through testing using the IEEE Reliability Test System (RTS). The test results confirm that the proposed approach can to ensure that the system security and outperforms the existing deterministic and stochastic optimization methods both in terms of the quality of the solution and the computational effort required. Therefore, the proposed methodology represents a particular effective technique for the UMSD.

Orientation field (OF) estimation is a fundamental process in fingerprint authentication systems. In this paper, a novel binary pattern based low-cost OF estimation algorithm is proposed. The new method consists of two modules. The first is block-level orientation estimation and averaging in vector space by pixel level orientation statistics. The second is orientation quantization and smoothing. In the second module, the continuous orientation is quantized into fixed orientations with sufficient resolution (interval between fixed orientations). An effective smoothing scheme on the quantized orientation space is also proposed. The proposed algorithm is capable of stably processing poor-quality fingerprint images and is validated by tests conducted on an adaptive OF matching scheme. The proposed algorithm is also implemented into a fingerprint System on Chip (SoC) to comfirm that it satisfies the strict requirements of embedded system.

A novel filter is introduced in this paper to improve the ability of radiometric based method on suppressing impulse noise. Firstly, a new method is introduced to design the impulsive weight by measuring how impulsive a pixel is. Then, the impulsive weight is combined with the radiometric weight to obtain the evaluated values on each pixel in the whole corrupted image. The impulsive weight is mainly designed to suppress the impulse noise, while the radiometric weight is mainly designed to protect the noise-free pixel. Extensive experiments demonstrate that the proposed algorithm can perform much better than other filters in terms of the quantitative and qualitative aspects.

In this letter, we propose a novel approach to human activity recognition. We present a class of features that are robust to the tilt of the attached sensor module and a state transition model suitable for HMM-based activity recognition. In addition, postprocessing techniques are applied to stabilize the recognition results. The proposed approach shows significant improvements in recognition experiments over a variety of human activity DB.

Acoustic feature transformation is widely used to reduce dimensionality and improve speech recognition performance. In this letter we focus on dimensionality reduction methods that minimize the average classification error. Unfortunately, minimization of the average classification error may cause considerable overlaps between distributions of some classes. To mitigate risks of considerable overlaps, we propose a dimensionality reduction method that minimizes the maximum classification error. We also propose two interpolated methods that can describe the average and maximum classification errors. Experimental results show that these proposed methods improve speech recognition performance.

This paper presents an automatic system for detecting and re-identifying people moving in different sites with non-overlapping views. We first propose an automatic process for silhouette extraction based on the combination of an adaptive background subtraction algorithm and a motion detection module. Such a combination takes advantage of both approaches and is able to tackle the problem of particular environments. The silhouette extraction results are then clustered based on their spatial belonging and colorimetric characteristics in order to preserve only the key regions that effectively represent the appearance of a person. The next important step consists in characterizing the extracted silhouettes by the appearance-based signatures. Our proposed descriptor, which includes both color and spatial feature of objects, leads to satisfying results compared to other descriptors in the literature. Since the passage of a person needs to be characterized by multiple frames, a large quantity of data has to be processed. Thus, a graph-based algorithm is used to realize the comparison of passages of people in front of cameras and to make the final decision of re-identification. The global system is tested on two real and difficult data sets recorded in very different environments. The experimental results show that our proposed system leads to very satisfactory results.

We develop a novel construction method for n-dimensional Hilbert space-filling curves. The construction method includes four steps: block allocation, Gray permutation, coordinate transformation and recursive construction. We use the tensor product theory to formulate the method. An n-dimensional Hilbert space-filling curve of 2r elements on each dimension is specified as a permutation which rearranges 2rn data elements stored in the row major order as in C language or the column major order as in FORTRAN language to the order of traversing an n-dimensional Hilbert space-filling curve. The tensor product formulation of n-dimensional Hilbert space-filling curves uses stride permutation, reverse permutation, and Gray permutation. We present both recursive and iterative tensor product formulas of n-dimensional Hilbert space-filling curves. The tensor product formulas are directly translated into computer programs which can be used in various applications. The process of program generation is explained in the paper.