We considered the oxygen diffusivity around a conductive filament of resistive switching oxides, with the aim of designing material appropriate for highly reliable non-volatile memory. Both theoretical and experimental analyses were performed for this consideration. The theoretically obtained oxygen chemical potential difference, which works as a driving force for diffusion, significantly depends on a material. Then, we experimentally confirmed that the oxygen diffusion behaviors vary greatly depending on the chemical potential differences.

Misbehaving nodes intrinsic to the physical vulnerabilities of ad-hoc sensor networks pose a challenging constraint on the designing of data fusion. To address this issue, a statistics-based reputation method for reliable data fusion is proposed in this study. Different from traditional reputation methods that only compute the general reputation of a node, the proposed method modeled by negative binomial reputation consists of two separated reputation metrics: fusion reputation and sensing reputation. Fusion reputation aims to select data fusion points and sensing reputation is used to weigh the data reported by sensor nodes to the fusion point. So, this method can prevent a compromised node from covering its misbehavior in the process of sensing or fusion by behaving well in the fusion or sensing. To tackle the unexpected facts such as packet loss, a discounting factor is introduced into the proposed method. Additionally, Local Outlier Factor (LOF) based outlier detection is applied to evaluate the behavior result of sensor nodes. Simulations show that the proposed method can enhance the reliability of data fusion and is more accurate than the general reputation method when applied in reputation evaluation.

The bilateral filter (BF) is a nonlinear and low-pass filter which can smooth an image while preserving detail structures. However, the filer is time consuming for real-time processing. In this paper, we bring forward a fresh idea that bilateral filtering can be accelerated by a multigrid (MG) scheme. Our method is based on the following two facts. a) The filtering result by a BF with a large kernel size on the original resolution can be approximated by applying a small kernel sized (3×3) version on the lower resolution many times on the premise of visual acceptance. Early work has shown that a BF can be viewed as nonlinear diffusion. The desired filtering result is actually an intermediate status of the diffusion process. b) Iterative linear equation techniques are sufficiently mature to cope with the nonlinear diffusion equation, which can be accelerated by the MG scheme. Experimental results with both simulated data sets and real sets are provided, and the new method is demonstrated to achieve almost twice the speed of the state-of-the-art. Compared with previous efforts for finding a generalized representation to link bilateral filtering and nonlinear diffusion by adaptive filtering, a novel relationship between nonlinear diffusion and bilateral filtering is explored in this study by focusing attention on numerical calculus.

This paper proposes a robust and fast lyric search method for music information retrieval (MIR). The effectiveness of lyric search systems based on full-text retrieval engines or web search engines is highly compromised when the queries of lyric phrases contain incorrect parts due to mishearing. To improve the robustness of the system, the authors introduce acoustic distance, which is computed based on a confusion matrix of an automatic speech recognition experiment, into Dynamic-Programming (DP)-based phonetic string matching to identify the songs that the misheard lyric phrases refer to. An evaluation experiment verified that the search accuracy is increased by 4.4% compared with the conventional method. Furthermore, in this paper a two-pass search algorithm is proposed to realize real-time execution. The algorithm pre-selects the probable candidates using a rapid index-based search in the first pass and executes a DP-based search process with an adaptive termination strategy in the second pass. Experimental results show that the proposed search method reduced processing time by more than 86.2% compared with the conventional methods for the same search accuracy.

With the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance, two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41%, which outperforms six traditional time- or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01%, which results in an improvement in accuracy of 4.14% compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.

A novel fusion method for semantic concept detection in images, called tree fusion, is proposed. Various kinds of features are given to different classifiers. Then, according to the importance of features and effectiveness of classifiers, the results of feature-classifier pairs are ranked and fused using C4.5 algorithm. Experimental results conducted on the MSRC and PASCAL VOC 2007 datasets have demonstrated the effectiveness of the proposed method over the traditional fusion methods.

Artificial blurring is a typical operation in image forging. Most existing image forgery detection methods consider only one single feature of artificial blurring operation. In this manuscript, we propose to adopt feature fusion, with multifeatures for artificial blurring operation in image tampering, to improve the accuracy of forgery detection. First, three feature vectors that address the singular values of the gray image matrix, correlation coefficients for double blurring operation, and image quality metrics (IQM) are extracted and fused using principal component analysis (PCA), and then a support vector machine (SVM) classifier is trained using the fused feature extracted from training images or image patches containing artificial blurring operations. Finally, the same procedures of feature extraction and feature fusion are carried out on the suspected image or suspected image patch which is then classified, using the trained SVM, into forged or non-forged classes. Experimental results show the feasibility of the proposed method for image tampering feature fusion and forgery detection.

This study proposes a feature-level fusion method that uses finger veins (FVs) and finger dorsal texture (FDT) for personal authentication based on orientation selection (OS). The orientation codes obtained by the filters correspond to different parts of an image (foreground or background) and thus different orientations offer different levels of discrimination performance. We have conducted an orientation component analysis on both FVs and FDT. Based on the analysis, an OS scheme is devised which combines the discriminative orientation features of both modalities. Our experiments demonstrate the effectiveness of the proposed method.

Biometric identification has recently attracted attention because of its convenience: it does not require a user ID nor a smart card. However, both the identification error rate and response time increase as the number of enrollees increases. In this paper, we combine a score level fusion scheme and a metric space indexing scheme to improve the accuracy and response time in biometric identification, using only scores as information sources. We firstly propose a score level indexing and fusion framework which can be constructed from the following three schemes: (I) a pseudo-score based indexing scheme, (II) a multi-biometric search scheme, and (III) a score level fusion scheme which handles missing scores. A multi-biometric search scheme can be newly obtained by applying a pseudo-score based indexing scheme to multi-biometric identification. We secondly propose the NBS (Naive Bayes search) scheme as a multi-biometric search scheme and discuss its optimality with respect to the retrieval error rate. We evaluated our proposal using the datasets of multiple fingerprints and face scores from multiple matchers. The results showed that our proposal significantly improved the accuracy of the unimodal biometrics while reducing the average number of score computations in both the datasets.

As a promising lamination-loss-free fabrication technique, diffusion bonding of etched thin metal plates is used to realize double-layer waveguide slot antennas. Alternating-phase feed is adopted in this paper to reduce the number of laminated plates to simplify fabrication as well as to reduce cost. A 20 × 20-element double-layer waveguide slot antenna with a bottom partially-corporate feed circuit is designed for 39GHz band operation as an example. The adjacent radiating waveguides as well as the 2 × 2 sub-arrays fed in an alternating-phase manner eliminate the need for complete electrical contact in the top layer. However, the feed circuit in the bottom layer has to be completely diffusion-bonded. These two layers are simply assembled by screws. An antenna laminated by only diffusion bonding is also fabricated and evaluated for comparison. The comparison proved that the simply fabricated antenna is comparable in performance to the fully diffusion-bonded one.

A point-to-point fixed wireless access (FWA) system with a maximum throughput of 1Gbps has been developed in the 39GHz band. A double-layer plate-laminated waveguide slot array antenna is successfully realized with specific considerations of practical application. The antenna is designed so as to hold the VSWR under 1.5. The antenna input as well as feeding network is configured to reduce the antenna profile as well as the antenna weight. In addition, integrating the antenna into a wireless terminal is taken into account. A shielding wall, whose effectiveness is experimentally demonstrated, is set in the middle of the wireless terminal to achieve the spatial isolation of more than 65dB between two antennas on the H-plane. 30 test antennas are fabricated by diffusion bonding of thin metal plates, to investigate the tolerance and mass-productivity of this process. An aluminum antenna, which has the advantages of light weight and anti-aging, is also fabricated and evaluated with an eye to the future.

In this paper, we propose a robust registration method, named Bounded-Variables Least Median of Squares (BVLMS). It overcomes both the misassociations and the ill-conditioning due to the interactions between Bounded-Variables Least Squares (BVLS) and Least Median of Squares (LMS). Simulation results demonstrate the feasibility of this new registration method.

This paper addresses the sensing-throughput tradeoff problem by using cluster-based cooperative spectrum sensing (CSS) schemes in two-layer hierarchical cognitive radio networks (CRNs) with soft data fusion. The problem is formulated as a combinatorial optimization problem involving both discrete and continuous variables. To simplify the solution, a reasonable weight fusion rule (WFR) is first optimized. Thus, the problem devolves into a constrained discrete optimization problem. In order to efficiently and effectively resolve this problem, a lexicographical approach is presented that solving two optimal subproblems consecutively. Moreover, for the first optimal subproblem, a closed-form solution is deduced, and an optimal clustering scheme (CS) is also presented for the second optimal subproblem. Numerical results show that the proposed approach achieves a satisfying performance and low complexity.

This letter presents a novel approach for automatic multimodal affect recognition. The audio and visual channels provide complementary information for human affective states recognition, and we utilize Boltzmann zippers as model-level fusion to learn intrinsic correlations between the different modalities. We extract effective audio and visual feature streams with different time scales and feed them to two component Boltzmann chains respectively. Hidden units of the two chains are interconnected to form a Boltzmann zipper which can effectively avoid local energy minima during training. Second-order methods are applied to Boltzmann zippers to speed up learning and pruning process. Experimental results on audio-visual emotion data recorded by ourselves in Wizard of Oz scenarios and collected from the SEMAINE naturalistic database both demonstrate our approach is robust and outperforms the state-of-the-art methods.

In this paper, an efficient multi-modal medical image fusion approach is proposed based on local features contrast and bilateral sharpness criterion in nonsubsampled contourlet transform (NSCT) domain. Compared with other multiscale decomposition analysis tools, the nonsubsampled contourlet transform not only can eliminate the “block-effect” and the “pseudo-effect”, but also can represent the source image in multiple direction and capture the geometric structure of source image in transform domain. These advantages of NSCT can, when used in fusion algorithm, help to attain more visual information in fused image and improve the fusion quality. At the same time, in order to improve the robustness of fusion algorithm and to improve the quality of the fused image, two selection rules should be considered. Firstly, a new bilateral sharpness criterion is proposed to select the lowpass coefficient, which exploits both strength and phase coherence. Secondly, a modified SML (sum modified Laplacian) is introduced into the local contrast measurements, which is suitable for human vision system and can extract more useful detailed information from source images. Experimental results demonstrate that the proposed method performs better than the conventional fusion algorithm in terms of both visual quality and objective evaluation criteria.

We design and fabricate a double-layer hollow-waveguide slot array antenna with wide bandwidth and high antenna efficiency for the 120 GHz band. The antenna is fabricated by diffusion bonding of laminated thin metal plates for high precision and perfect electrical contact. The 1616-element antenna shows more than 70% antenna efficiency over a 13 GHz bandwidth. Furthermore, it realizes error-free data transmission in 2.5 m distance at up to 10 Gbit/s. To our knowledge, this is the first report of the design and fabrication of a high-efficiency wideband planar antenna for the 120 GHz band.

Diffusion-weighted (DW)-functional magnetic resonance imaging (fMRI) is a recently reported technique for measuring neural activities by using diffusion-weighted imaging (DWI). DW-fMRI is based on the property that cortical cells swell when the brain is activated. This approach can be used to observe changes in water diffusion around cortical cells. The spatial and temporal resolutions of DW-fMRI are superior to those of blood-oxygenation-level-dependent (BOLD)-fMRI. To investigate how the DWI signal intensities change in DW-fMRI measurement, we carried out Monte Carlo simulations to evaluate the intensities before and after cell swelling. In the simulations, we modeled cortical cells as two compartments by considering differences between the intracellular and the extracellular regions. Simulation results suggested that DWI signal intensities increase after cell swelling because of an increase in the intracellular volume ratio. The simulation model with two compartments, which respectively represent the intracellular and the extracellular regions, shows that the differences in the DWI signal intensities depend on the ratio of the intracellular and the extracellular volumes. We also investigated the MPG parameters, b-value, and separation time dependences on the percent signal changes in DW-fMRI and obtained useful results for DW-fMRI measurements.

A novel and energy-efficient algorithm with Quality-of-Service (QoS) guarantee is proposed for cooperative spectrum sensing (CSS) with soft information fusion and hard information fusion. By weighting the sensing performance and the consumption of system resources in a utility function that is maximized with respect to the number of secondary users (SUs), it is shown that the optimal number of SUs is related to the price of these QoS requirements.

Objective: The virtual slides are high-magnification whole digital images of histopathological tissue sections. The existing virtual slide system, which is optimized for scanning flat and smooth plane slides such as histopathological paraffin-embedded tissue sections, but is unsuitable for scanning irregular plane slides such as cytological smear slides. This study aims to develop a virtual slide system suitable for cytopathology slide scanning and to evaluate the effectiveness of multi-focus image fusion (MF) in cytopathological diagnosis. Study Design: We developed a multi-layer virtual slide scanning system with MF technology. Tumors for this study were collected from 21 patients diagnosed with primary breast cancer. After surgical extraction, smear slide for cytopathological diagnosis were manufactured by the conventional stamp method, fine needle aspiration method (FNA), and tissue washing method. The stamp slides were fixed in 95% ethanol. FNA and tissue washing samples were fixed in CytoRich RED Preservative Fluid, a liquid-based cytopathology (LBC). These slides were stained with Papanicolaou stain, and scanned by virtual slide system. To evaluate the suitability of MF technology in cytopathological diagnosis, we compared single focus (SF) virtual slide with MF virtual slide. Cytopathological evaluation was carried out by 5 pathologists and cytotechnologists. Results: The virtual slide system with MF provided better results than the conventional SF virtual slide system with regard to viewing inside cell clusters and image file size. Liquid-based cytology was more suitable than the stamp method for virtual slides with MF. Conclusion: The virtual slide system with MF is a useful technique for the digitization in cytopathology, and this technology could be applied to tele-cytology and e-learning by virtual slide system.

A novel cooperative spectrum sensing scheme suitable for wireless cognitive radio system with imperfect reporting channels is proposed. In the proposed scheme, binary local decision bits are transmitted to the fusion center and combined to form a soft-valued decision statistics in the fusion center. To form a decision statistics, a majority-decision-aided weighting rule is proposed. The proposed scheme provides a reliable sensing capability even with poor reporting channels.