A black mask (BM) is a layer used to improve the display quality by suppressing light leakage. In general, the BM is formed by a photolithography process. In this study, a novel technique for the fabrication of a quasi-black mask (q-BM) is proposed; the q-BM was composed of vertical and hybrid orientation areas, patterned by a separation coating technique using an electro-spray deposition method. Using our technique, the q-BM can be formed easily without the additional masks used for the BM.

The segmentation of Mycobacterium tuberculosis images forms the basis for the computer-aided diagnosis of tuberculosis. The segmented objects are often broken due to the low-contrast objects and the limits of segmentation method. This will result in decreasing the accuracy of segmentation and recognition. A simple and effective post-processing method is proposed to connect the broken objects. The broken objects in the segmented binary images are connected based on the information obtained from their skeletons. Experimental results demonstrate the effectiveness of our proposed method.

We developed flexible LC devices using coat-debond polyimide substrates with a low birefringence and etched post spacers, and clarified that flexible LCDs using post spacers with small spacer distance have a high flexibility without degradation of the image quality. This result ensured the feasibility of flexible LCDs using coat-debond method.

We have proposed composite films composed of a molecular-aligned polymer and liquid crystal (LC) for substrate-free liquid crystal displays with high-contrast images. We successfully controlled the molecular alignment of the LC and formed molecular-aligned LC droplets in the polymer by controlling the fluidity of the LC/monomer mixture and the curing rate of the monomer.

We propose a speech analysis method based on the source-filter model using multivariate empirical mode decomposition (MEMD). The proposed method takes multiple adjacent frames of a speech signal into account by combining their log spectra into multivariate signals. The multivariate signals are then decomposed into intrinsic mode functions (IMFs). The IMFs are divided into two groups using the peak of the autocorrelation function (ACF) of an IMF. The first group characterized by a spectral fine structure is used to estimate the fundamental frequency F0 by using the ACF, whereas the second group characterized by the frequency response of the vocal-tract filter is used to estimate formant frequencies by using a peak picking technique. There are two advantages of using MEMD: (i) the variation in the number of IMFs is eliminated in contrast with single-frame based empirical mode decomposition and (ii) the common information of the adjacent frames aligns in the same order of IMFs because of the common mode alignment property of MEMD. These advantages make the analysis more accurate than with other methods. As opposed to the conventional linear prediction (LP) and cepstrum methods, which rely on the LP order and cut-off frequency, respectively, the proposed method automatically separates the glottal-source and vocal-tract filter. The results showed that the proposed method exhibits the highest accuracy of F0 estimation and correctly estimates the formant frequencies of the vocal-tract filter.

A novel method for illumination-invariant face representation is presented based on the orthogonal decomposition of the local image structure. One important advantage of the proposed method is that image gradients and corresponding intensity values are simultaneously used with our decomposition procedure to preserve the original texture while yielding the illumination-invariant feature space. Experimental results demonstrate that the proposed method is effective for face recognition and verification even with diverse lighting conditions.

In 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) systems, when HARQ (Hybrid Automatic Repeat request) retransmission is invoked, the data at the transmitter are retransmitted randomly or sequentially regardless of their relationship to the wrongly decoded data. Such practice is inefficient since precious transmission resources will be spent to retransmit data that may be of no use in error correction at the receiver. This paper proposes an incremental redundancy HARQ scheme based on Error Position Estimating Coding (ePec) and LDPC (Low Density Parity Check Code) channel coding, which is called ePec-LDPC HARQ. The proposal is able to feedback the wrongly decoded code blocks within a specific MAC (Media Access Control) PDU (Protocol Data Unit) from the receiver. The transmitter gets the feedback information and then performs targeted retransmission. That is, only the data related to the wrongly decoded code blocks are retransmitted, which can improve the retransmission efficiency and thus reduce the retransmission overload. An enhanced incremental redundancy LDPC coding approach, called EIR-LDPC, together with a physical layer framing method, is developed to implement ePec-LDPC HARQ. Performance evaluations show that ePec-LDPC HARQ reduces the overall transmission resources by 15% compared to a conventional LDPC HARQ scheme. Moreover, the average retransmission times of each MAC PDU and the transmission delay are also reduced considerably.

Exploring a parsimonious model that is just enough to represent the temporal dependency of time serial signals such as audio or speech is a practical requirement for many signal processing applications. A well suited method for intuitively and efficiently representing magnitude spectra is to use convolutive non-negative matrix factorization (CNMF) to discover the temporal relationship among nearby frames. However, the model order selection problem in CNMF, i.e., the choice of the number of convolutive bases, has seldom been investigated ever. In this paper, we propose a novel Bayesian framework that can automatically learn the optimal model order through maximum a posteriori (MAP) estimation. The proposed method yields a parsimonious and low-rank approximation by removing the redundant bases iteratively. We conducted intuitive experiments to show that the proposed algorithm is very effective in automatically determining the correct model order.

A novel virtual sensors-based positioning method has been presented in this paper, which can make use of both direct paths and indirect paths. By integrating the virtual sensor idea and Bayesian state and observation framework, this method models the indirect paths corresponding to persistent virtual sensors as virtual direct paths and further reformulates the wireless positioning problem as the maximum likelihood estimation of both the mobile terminal's positions and the persistent virtual sensors' positions. Then the method adopts the EM (Expectation Maximization) and the particle filtering schemes to estimate the virtual sensors' positions and finally exploits not only the direct paths' measurements but also the indirect paths' measurements to realize the mobile terminal's positions estimation, thus achieving better positioning performance. Simulation results demonstrate the effectiveness of the proposed method.

In this letter, a novel channel impulse response (CIR)-based fingerprinting positioning method using kernel principal component analysis (KPCA) has been proposed. During the offline phase of the proposed method, a survey is performed to collect all CIRs from access points, and a fingerprint database is constructed, which has vectors including CIR and physical location. During the online phase, KPCA is first employed to solve the nonlinearity and complexity in the CIR-position dependencies and extract the principal nonlinear features in CIRs, and support vector regression is then used to adaptively learn the regress function between the KPCA components and physical locations. In addition, the iterative narrowing-scope step is further used to refine the estimation. The performance comparison shows that the proposed method outperforms the traditional received signal strength based positioning methods.

We propose an edge-preserving multiscale image decomposition method using filters for non-equispaced signals. It is inspired by the domain transform, which is a high-speed edge-preserving smoothing method, and it can be used in many image processing applications. One of the disadvantages of the domain transform is sensitivity to noise. Even though the proposed method is based on non-equispaced filters similar to the domain transform, it is robust to noise since it employs a multiscale decomposition. It uses the Laplacian pyramid scheme to decompose an input signal into the piecewise-smooth components and detail components. We design the filters by using an optimization based on edge-preserving smoothing with a conversion of signal distances and filters taking into account the distances between signal intervals. In addition, we also propose construction methods of filters for non-equispaced signals by using arbitrary continuous filters or graph spectral filters in order that various filters can be accommodated by the proposed method. As expected, we find that, similar to state-of-the-art edge-preserving smoothing techniques, including the domain transform, our approach can be used in many applications. We evaluated its effectiveness in edge-preserving smoothing of noise-free and noisy images, detail enhancement, pencil drawing, and stylization.

The limitation of the GPS in urban canyon has led to the rapid development of Wi-Fi positioning system (WPS). The fingerprint-based WPS could be divided into calibration and positioning stages. In calibration stage, several grid points (GPs) are selected, and their position tags and featured access points (APs) are collected to build fingerprint database. In positioning stage, real time measurement of APs are compared with the feature of each GP in the database. The k weighted nearest neighbors (KWNN) algorithm is used as pattern matching algorithm to estimate the final positioning result. However, the performance of outdoor fingerprint-based WPS is not good enough for pedestrian navigation. The main challenge is to build a robust fingerprint database. The received number of APs in outdoor environments has large variation. In addition, positioning result estimated by GPS receiver is used as position tag of each GP to automatically build the fingerprint database. This paper studies the lifecycle of fingerprint database in outdoor environment. We also shows that using long time collected data to build database could improve the positioning accuracy. Moreover, a new 3D-GNSS (3D building models aided GNSS) positioning method is used to provide accurate position tags. In this paper, the fingerprint-based WPS has been developed in an outdoor environment near the center of Tokyo city. The proposed WPS can achieve around 17 meters positioning accuracy in urban canyon.

This paper proposes a blind, inaudible, robust digital-audio watermarking scheme based on singular-spectrum analysis, which relates to watermarking techniques based on singular value decomposition. We decompose a host signal into its oscillatory components and modify amplitudes of some of those components with respect to a watermark bit and embedding rule. To improve the sound quality of a watermarked signal and still maintain robustness, differential evolution is introduced to find optimal parameters of the proposed scheme. Test results show that, although a trade-off between inaudibility and robustness still persists, the difference in sound quality between the original and the watermarked one is considerably smaller. This improved scheme is robust against many attacks, such as MP3 and MP4 compression, and band-pass filtering. However, there is a drawback, i.e., some music-dependent parameters need to be shared between embedding and extraction processes. To overcome this drawback, we propose a method for automatic parameter estimation. By incorporating the estimation method into the framework, those parameters need not to be shared, and the test results show that it can blindly decode watermark bits with an accuracy of 99.99%. This paper not only proposes a new technique and scheme but also discusses the singular value and its physical interpretation.

Estimation of the time delay of arrival (TDOA) problem is important to acoustic source localization. The TDOA estimation problem is defined as finding the relative delay between several microphone signals for the direct sound. To estimate TDOA, the generalized cross-correlation (GCC) method is the most frequently used, but it has a disadvantage in terms of reverberant environments. In order to overcome this problem, the adaptive eigenvalue decomposition (AED) method has been developed, which estimates the room transfer function and finds the direct-path delay. However, the algorithm does not take into account the fact that the room transfer function is a sparse channel, and so sometimes the estimated transfer function is too dense, resulting in failure to exact direct-path and delay. In this paper, an enhanced AED algorithm that makes use of a proportionate step-size control and a direct-path constraint is proposed instead of a constant step size and the L2-norm constraint. The simulation results show that the proposed algorithm has enhanced performance as compared to both the conventional AED method and the phase-transform (PHAT) algorithm.

Energy-efficient resource allocation is considered in sensing-based spectrum sharing for cooperative cognitive radio networks (CCRNs). The secondary user first listens to the spectrum allocated to the primary user (PU) to detect the PU state and then initiates data transmission with two power levels based on the sensing decision (e.g., idle or busy). Under this model, the optimization problem of maximizing energy efficiency (EE) is formulated over the transmission power and sensing time subject to some practical limitations, such as the individual power constraint for secondary source and relay, the quality of service (QoS) for the secondary system, and effective protection for the PU. Given the complexity of this problem, two simplified versions (i.e., perfect and imperfect sensing cases) are studied in this paper. We transform the considered problem in fractional form into an equivalent optimization problem in subtractive form. Then, for perfect sensing, the Lagrange dual decomposition and iterative algorithm are applied to acquire the optimal power allocation policy; for imperfect sensing, an exhaustive search and iterative algorithm are proposed to obtain the optimal sensing time and corresponding power allocation strategy. Finally, numerical results show that the energy-efficient design greatly improves EE compared with the conventional spectrum-efficient design.

In this paper, we propose a single-channel speech enhancement method for a push-to-talk enabled wireless communication device. The proposed method is based on adaptive weighted β-order spectral amplitude estimation under speech presence uncertainty and enhanced instantaneous phase estimation in order to achieve flexible and effective noise reduction while limiting the speech distortion due to different noise conditions. Experimental results confirm that the proposed method delivers higher voice quality and intelligibility than the reference methods in various noise environments.

In this work, the novel fingerprinting evaluation parameter, which is called the punishment cost, is proposed. This parameter can be calculated from the designed matrix, the punishment matrix, and the confusion matrix. The punishment cost can describe how well the result of positioning is in the designated grid or not, by which the conventional parameter, the accuracy, cannot describe. The experiment is done with real measured data on weekdays and weekends. The results are considered in terms of accuracy and the punishment cost. Three well-known machine learning algorithms, i.e. Decision Tree, k-Nearest Neighbors, and Artificial Neural Network, are verified in fingerprinting positioning. In experimental environment, Decision Tree can perform well on the data from weekends whereas the performance is underrated on the data from weekdays. The k-Nearest Neighbors has proper punishment costs, even though it has lower accuracy than that of Artificial Neural Network, which has moderate accuracies but lower punishment costs. Therefore, other criteria should be considered in order to select the algorithm for indoor positioning. In addition, punishment cost can facilitate the conversion spot positioning to floor positioning without data modification.

In this paper, we propose an infrastructure-free precise positioning system by utilizing a variation of received radio broadcast signal strength against vehicle travel as fingerprints of road segments. Use of broadcast wave is considered advantageous in deployment cost and sample density that affects measurement reliability, compared to communication medium such as 802.11p-based V2X radio used in our previous paper. We also present preliminary experimental results that indicate potential of positioning at 20cm accuracy by using reception information of two FM radio channels broadcast from a station about 20km away from the test track

Ternary content addressable memory (TCAM), which can store 0, 1, or X in its cells, is widely used to store routing tables in network routers. Negative bias temperature instability (NBTI) and positive bias temperature instability (PBTI), which increase Vth and degrade transistor switching speed, have become major reliability challenges. This study analyzes the signal probability of routing tables. The results show that many cells retain static stress and suffer significant degradation caused by NBTI and PBTI effects. The bit flipping technique is improved and proactive power gating recovery is proposed to mitigate NBTI and PBTI effects. In order to maintain the functionality of TCAM after bit flipping, a novel TCAM cell design is proposed. Simulation results show that compared to the original architecture, the bit flipping technique improves read static noise margin (SNM) for data and mask cells by 16.84% and 29.94%, respectively, and reduces search time degradation by 12.95%. The power gating technique improves read SNM for data and mask cells by 12.31% and 20.92%, respectively, and reduces search time degradation by 17.57%. When both techniques are used, read SNM for data and mask cells is improved by 17.74% and 30.53%, respectively, and search time degradation is reduced by 21.01%.

The dyadic filter bank property of multivariate empirical mode decomposition (MEMD) for white Gaussian noise (WGN) is well established. In order to investigate the way MEMD behaves in the presence of fractional Gaussian noise (fGn), we conduct thorough numerical experiments for MEMD for fGn inputs. It turns out that similar to WGN, MEMD follows dyadic filter bank structure for fGn inputs, which is more stable than empirical mode decomposition (EMD) regardless of the Hurst exponent. Moreover, the estimation of the Hurst exponent of fGn contaminated with different kinds of signals is also presented via MEMD in this work.