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Gait and physical fitness are related to cognitive function. A decrease in motor function and physical fitness can serve as an indicator of declining global cognitive function in older adults. This study aims to use machine learning (ML) to identify important features of gait and physical fitness to predict a decline in global cognitive function in older adults. A total of three hundred and six participants aged seventy-five years or older were included in the study, and their gait performance at various speeds and physical fitness were evaluated. Eight ML models were applied to data ranked by the p-value (LP) of linear regression and the importance gain (XI) of XGboost. Five optimal features were selected using elastic net on the LP data for men, and twenty optimal features were selected using support vector machine on the XI data for women. Thus, the important features for predicting a potential decline in global cognitive function in older adults were successfully identified herein. The proposed ML approach could inspire future studies on the early detection and prevention of cognitive function decline in older adults.

The elderly population in South Korea accounted for 15.5% of the total population in 2019. Thus, it is important to study the various elements governing the process of healthy aging. Therefore, this study investigated multiple prediction models to determine the health-related quality of life (HRQoL) in elderly adults based on the demographics, questionnaires, gait ability, and physical fitness. We performed eight physical fitness tests on 775 participants wearing shoe-type inertial measurement units and completing walking tasks at slower, preferred, and faster speeds. The HRQoL for physical and mental components was evaluated using a 36-item, short-form health survey. The prediction models based on multiple linear regression with feature importance were analyzed considering the best physical and mental components. We used 11 variables and 5 variables to form the best subset of features underlying the physical and mental components, respectively. We laid particular emphasis on evaluating the functional endurance, muscle strength, stress level, and falling risk. Furthermore, stress, insomnia severity, number of diseases, lower body strength, and fear of falling were taken into consideration in addition to mental-health-related variables. Thus, the study findings provide reliable and objective results to improve the understanding of HRQoL in elderly adults.

PNN* for Intelligent 3D Vision • Intelligent 3D Vision on Mobile Devices • Accurate & Robust Perception with 3D Structural Information • Mobile 3D Sensor Already Commercialized

An efficient and high-speed 3D point cloud-based neural network processing unit (PNNPU) is proposed using the block-based point processing. It has three key features: 1) page-based point block memory management unit (PMMU) with linked list-based page table (LLPT) for on-chip memory footprint reduction, 2) hierarchical block-wise farthest point sampling (HFPS), and block skipping ball-query (BSBQ) for fast and efficient point processing, 3) Skipping-based max-pooling prediction (SMPP) for throughput enhancement. The PNNPU is fabricated in 65nm CMOS process and evaluated on the 3D object detection (3D OD) application. As a result, it shows 84.8 fps at 266.8mW power consumption and achieving 6.6-11.9 TOPS/W energy efficiency.

This paper presents a planar transformer design of a 3-kW 250 kHz SiC-based Auxiliary Power Module (APM) with 3.6 kW/L power density for electric vehicle (xEV) application. The transformer design is optimized based on the trade-off between footprint and magnetic loss using the proposed figure-of-merit (FoM) based optimization. In order to increase the ZVS range of the converter, an additional leakage inductance is implemented using the proposed magnetic integration technique. A comparison between non-integrated and integrated magnetic cores using finite element analysis (FEA) is presented. Results show that the integrated structure can reduce the core loss up to 35% and footprint around 15 % compared to the non-integrated. Experimental results show that all the switches can achieves ZVS under the entire load condition.

Objectives We investigated the endoplasmic reticulum (ER) stress markers C/EBP homologous protein (CHOP) and glucose-regulated protein (GRP) 78, as well as the inflammatory factors nuclear factor (NF)-κB and IκBα, to assess how social defeat stress induces myocardial injury. Furthermore, we evaluated the protective effects of the ER stress inhibitor 4-phenylbutyric acid (PBA) on myocardial injury in mice. Methods Adult mice were divided into control, control + PBA, social defeat, and social defeat + PBA groups. The social defeat and social defeat + PBA groups were exposed to social defeat stress for 10 days. Cardiac tissues from all groups were analyzed after social defeat stress. H9C2 cells were used to detect the role of the ER stress agonist thapsigargin on expression of ER stress and inflammatory markers. Results Social defeat stress promoted apoptosis of cardiomyocytes, increased CHOP, NF-κB and, phospho-NF-κB protein expression, and decreased GRP78 and IκBα protein expression. Moreover, PBA significantly reversed these changes and attenuated thapsigargin-induced increased expression of CHOP and phospho-NF-κB, and decreased IκBα expression in H9C2 cells. Conclusions Social defeat stress initiates ER stress, promotes expression of inflammatory factors, and induces myocardial injury. Inhibiting ER stress could protect the myocardium from social defeat stress-induced myocardial injury.

The Illumina Infinium HumanMethylation27 (Illumina 27K) BeadChip assay is a relatively recent high-throughput technology that allows over 27,000 CpGs to be assayed. The Illumina 27K methylation data is less commonly used in comparison to gene expression in bioinformatics. It provides a critical need to find the optimal feature ranking (FR) method for handling the high dimensional data. The optimal FR method on the classifier is not well known, and choosing the best performing FR method becomes more challenging in high dimensional data setting. Therefore, identifying the statistical methods which boost the inference is of crucial importance in this context. This paper describes the detailed performances of FR methods such as fisher score, information gain, chi-square, and minimum redundancy and maximum relevance on different classification methods such as Adaboost, Random Forest, Naive Bayes, and Support Vector Machines. Through simulation study and real data applications, we show that the fisher score as an FR method, when applied on all the classifiers, achieved best prediction accuracy with significantly small number of ranked features.

Bisphenol A (BPA) is an endocrine-disrupting compound (EDC) that is used widely in commercial products in the production of polycarbonate plastics for baby and water bottles, epoxy resins for lacquer lining of food and beverage cans and water pipes, dental sealants, dental composites and thermal receipts paper. There is inhibitory effect of BPA on nuclear estrogen (E2) production in granulosa cells of developing follicles that disrupt normal development to the antral follicles via suppression of E2 in granulosa cells of developing follicles during the menstrual cycle followed by reduction in the number of oocytes retrieved in in-vitro fertilization (IVF) patients. Several studies corroborate an inverse association between serum and/or urinary BPA concentration and the IVF outcome: Peak E2 levels and the number of oocytes retrieved. Upon oral ingestion, 99.5% of unconjugated parent BPA (free BPA) is metabolized to either BPA glucuronide (BPA-G) or BPA sulfate (BPA-S). The unconjugated BPA can bind to the estrogen receptors (ER) while conjugated BPA (biologically inactive BPA) do not bind the estrogen receptor (ER). The challenge is to assess the relationship between BPA exposure among infertile patients with respect to follicular response and health during IVF. The establishment of temporal sequence between BPA exposure and infertility would be the research question to answer: Which route is a better biomarker? The advantages of urine BPA collection would provide pragmatic advantages for clinicians in order to practice cost-effective medicine. However, unconjugated BPA measurement (compared to total BPA) introduces challenges in measurement accuracy since unconjugated BPA requires higher magnitude of limit of detection (LOD) with higher risk of contamination from the medical equipment. The difference in route of BPA assessment could introduce bias in the interpretation of results in terms of the association between BPA levels and the number of oocytes. Fujimoto et al. and Bloom et al. analyzed the relationship between serum BPA and IVF outcome in infertile women. It may sound hypothetically justified due to utilizing serum unconjugated BPA, this strategy is not successful in choosing a practical biomarker of BPA exposure due to toxicokinetic properties of BPA metabolism and excretion in humans.

The success of vehicular ad hoc network (VANET) among vehicle consumers is subject to the quality of comfort and realism of safety promised by this technology. Recently VANET evolved to a rather more application and services-rich paradigm referred to as VANET-based clouds. However, the initial deployment stage of VANET and its successor VANET-based cloud is going to be a daunting challenge due to less market penetration rate of the technology-enabled vehicles, and the deployment and cost of road-side infrastructure. To fill the gaps, in this paper, after arguing on the predictability of spatiotemporal characteristics of the public transport buses in urban areas, we propose a mechanism where these buses are used as mobile gateways (MGs) among vehicles on the road, VANET authorities, and the cloud infrastructure. MGs work as functional entities of the mobile intermediary infrastructure (MII) in VANET-based clouds. Our proposed scheme can serve as a feasible, cost-effective, and pre-established MII for standalone VANET and VANET-based clouds. We furthermore, carry out feasibility analysis through a communication scheme in VANET-based clouds. More precisely we consider the traffic information aggregation and dissemination in VANET-based clouds. In order to argue on the feasibility of buses as MGs, we consider real-time road network dynamics in Seoul, South Korea where the public buses provide perfect connectivity to other vehicular nodes in the neighborhood. In VANET-based clouds application, vehicles share coarse-grained information with clouds through MGs and receive fine-grained traffic information from cloud infrastructure through MGs in real-time. Our simulation results show that MGs provide almost 100% coverage in average traffic scenarios and about 98% coverage in worst traffic scenarios. These MGs also provide the vehicles with about 84% traffic information in worst case and over 90% traffic information in average traffic scenarios. Our proposed infrastructure can be a strong rationale for the initial deployment of these technologies and can possibly be a reasonable partial or full replacement for static RSUs in the urban scenarios.

In high-dimensional data, the performances of various classifiers are largely dependent on the selection of important features. Most of the individual classifiers with the existing feature selection (FS) methods do not perform well for highly correlated data. Obtaining important features using the FS method and selecting the best performing classifier is a challenging task in high throughput data. In this article, we propose a combination of resampling-based least absolute shrinkage and selection operator (LASSO) feature selection (RLFS) and ensembles of regularized regression (ERRM) capable of dealing data with the high correlation structures. The ERRM boosts the prediction accuracy with the top-ranked features obtained from RLFS. The RLFS utilizes the lasso penalty with sure independence screening (SIS) condition to select the top k ranked features. The ERRM includes five individual penalty based classifiers: LASSO, adaptive LASSO (ALASSO), elastic net (ENET), smoothly clipped absolute deviations (SCAD), and minimax concave penalty (MCP). It was built on the idea of bagging and rank aggregation. Upon performing simulation studies and applying to smokers&rsquo, cancer gene expression data, we demonstrated that the proposed combination of ERRM with RLFS achieved superior performance of accuracy and geometric mean.

In this paper, a 1.8kW, 200V-310V/12V-15V,700kHz DC-DC converter with high power density of 8.1kW/L (132.7W/in3) is developed for electric/hybrid vehicle application. In order to achieve high power density, GaN-based transistors at 700kHz switching frequency are applied to reduce passive component volumes. Furthermore, reduction of planar transformer size is achieved by applying the concept of the matrix transformer with flux cancellation. The characteristics of the 1st prototype and the 2nd prototype considered to improve the power density are covered and the design considerations of the gate driver for a high switching frequency of 700 kHz are covered. A three-dimensional (3D) Maxwell FEA simulation results are presented to give an accurate estimation of the core loss and flux distribution of the magnetic components and experimental results are also provided from a 1.8kW prototype.

In high-dimensional gene expression data analysis, the accuracy and reliability of cancer classification and selection of important genes play a very crucial role. To identify these important genes and predict future outcomes (tumor vs. non-tumor), various methods have been proposed in the literature. But only few of them take into account correlation patterns and grouping effects among the genes. In this article, we propose a rank-based modification of the popular penalized logistic regression procedure based on a combination of ℓ 1 and ℓ 2 penalties capable of handling possible correlation among genes in different groups. While the ℓ 1 penalty maintains sparsity, the ℓ 2 penalty induces smoothness based on the information from the Laplacian matrix, which represents the correlation pattern among genes. We combined logistic regression with the BH-FDR (Benjamini and Hochberg false discovery rate) screening procedure and a newly developed rank-based selection method to come up with an optimal model retaining the important genes. Through simulation studies and real-world application to high-dimensional colon cancer gene expression data, we demonstrated that the proposed rank-based method outperforms such currently popular methods as lasso, adaptive lasso and elastic net when applied both to gene selection and classification.

This paper presents a technique to achieve a high power density of a 1.8kW low output voltage, high current dc-dc converter for xEV application. Using a GaN-based transistor at 700kHz switching frequency, the size of the passive component can be greatly reduced. The concept of matrix planar transformer with flux cancellation is applied, resulting in a reduction of the transformer size. Topologies evaluation are performed to select the most suitable topology for achieving the size specification, and a phase-shift full-bridge converter with current-doubler rectifier is selected. A 5.63kW/L(92W/in3) power density is achieved. Simulation and experimental results from the proposed converter system prototype are provided.

Electric vehicles (EVs) can be considered as a revolution in the combustion industry with significant improvement in fuel utilization and decrease in pollution compared to combustion engines. However, by decreasing the size of the battery to reduce the cost, the frequency of charging EVs in a day increases. Therefore, to reduce the downtime required for charging EVs, wireless charging on the move can be an effective solution. In such a situation, paying for wireless charging on the move is an important issue. However, it can endanger the location privacy of users, since the EVs need to charge frequently in a day. In this paper, we first explain different methods of payment and problems with such payment methods in the case of wireless charging on the move. Then, we propose an efficient payment method based on `tokens' for wireless charging on the move, which minimizes the communications between service providers and users during the charging process. The proposed scheme prevents users and service providers from cheating, and it is robust to support different values for the price. Finally, we compare it with other payment methods that have been proposed for plug-in electric vehicles.

The effect of using a novel additional anti-vibration sleeper track (AAST) to reduce low-frequency vibrations (40–80 Hz) is investigated via numerical and field tests. A computational model is used to investigate the effects of sleeper spacing and track support stiffness. To verify the performance of the AAST, a field test was performed. The results confirmed that sleeper spacing and track support stiffness could be adjusted to control the vibration of subgrade in the targeted low-frequency range. The numerical and field test results demonstrated that the AAST can effectively reduce the low-frequency vibrations by reducing vibration transmission to the subgrade.

The molecular big data are highly correlated, and numerous genes are not related. The various classification methods performance mainly rely on the selection of significant genes. Sparse regularized regression (SRR) models using the least absolute shrinkage and selection operator (lasso) and adaptive lasso (alasso) are popularly used for gene selection and classification. Nevertheless, it becomes challenging when the genes are highly correlated. Here, we propose a modified adaptive lasso with weights using the ranking-based feature selection (RFS) methods capable of dealing with the highly correlated gene expression data. Firstly, an RFS methods such as Fisher’s score (FS), Chi-square (CS), and information gain (IG) are employed to ignore the unimportant genes and the top significant genes are chosen through sure independence screening (SIS) criteria. The scores of the ranked genes are normalized and assigned as proposed weights to the alasso method to obtain the most significant genes that were proven to be biologically related to the cancer type and helped in attaining higher classification performance. With the synthetic data and real application of microarray data, we demonstrated that the proposed alasso method with RFS methods is a better approach than the other known methods such as alasso with filtering such as ridge and marginal maximum likelihood estimation (MMLE), lasso and alasso without filtering. The metrics of accuracy, area under the receiver operating characteristics curve (AUROC), and geometric mean (GM-mean) are used for evaluating the performance of the models.

The Rapid development of 3D printing and additive manufacturing (AM) technology has spurred the 4th industrial revolution. As a result of the convergence of single line design (SLD) and the economically favored screw type extrusion (STE) process, non-Newtonian fluid foods were accurately controlled and 3D printed while including artistic approaches. The STE method with SLD using varying viscosities from 10 – 5,000 Pa•s of food was in the range of ± 21% and ± 57% accuracy without SLD. STE would be used in fields requiring nutritional management. STE has confirmed that the nutritional controlled diet of the general public or patients who need food control is successfully supported.

Today's high-end vehicles are technologically more sophisticated and smarter than the `old machines with engine and wheels'. These vehicles are anticipated to provide consumers with safe, reliable, and infotainment-rich driving experience through intelligent transportation system, realized through a technology referred to as vehicular ad hoc network (VANET). VANET offers a plethora of applications to the consumers ranging from safety to infotainment. Among other messages defined by the dedicated short range communication standard, cooperative awareness messages (CAMs) are of paramount importance for the VANET applications. In VANET, vehicles share their frequent mobility information in the form of CAMs with neighbors for cooperative awareness, traffic view construction, maneuver, and cruise control. In this work we target the CAM-based traffic information dissemination. VANET applications construct short-range local and long-range extend traffic views with the information contained in scheduled CAMs (also known as beacons). To construct aforementioned traffic views, naive approach is multi-hop communication; however, historically multi-hop communication does not scale efficiently in dynamic VANET due to computational and communication overhead. Therefore another alternative approach is essential to address the problems of the multi-hop communication in highly ephemeral VANET and to achieve maximum degree of the information dissemination in VANET. To fill the gap, in this paper we propose a geocast-based abstract piggybacking mechanism for traffic information dissemination in VANET through single-hop CAMs. In the proposed scheme, vehicles compress their local traffic view constructed through single-hop CAMs. The compressed local traffic view is referred to as traffic data unit (TDU). Every vehicle broadcasts its own TDU along with a number of TDUs received from other vehicles in the next scheduled CAM, in order to construct a long-range extended traffic view, also known as compressed data word (CDW). In other words, CDW is encapsulated into normal CAM in order to share it with the neighbors in the vicinity. The receivers of CDW use the received TDUs to construct the desired length of the long-range extended traffic view. The proposed scheme leverages only single-hop beacons data to construct both local and extended traffic views. Our simulation results show that the extended traffic view up to 1 km can be constructed with only single-hop beacons at the expense of 10 % of extra information with the regular CAM. The results also show that the effect of the extra overhead is not phenomenal from overhead standpoint. The proposed virtual multi-hop communication paradigm with single-hop CAM will reduce the overall communication thereby saving the bandwidth for other important safety related communications.

A computational approach to depth estimations using a color over lapped integral imaging system is presented. The proposed imaging system acquires multiple color images simultaneously through a single lens with an array of multiple pinholes that are distributed around the optical axis. This paper proposes a computational model of the relationship between the real distance of an object and the disparity among different color images. The proposed model can serve as a computational basis of a single camera-based depth estimation.

This paper proposes a new broadcasting system for the service-compatible 3DTV in which the 3D service can coexist with the conventional digital TV broadcast. In the proposed system, the commercial 3DTV service can be implemented via the existing DTV channel without utilizing the dedicated 3DTV system. This 2D/3D system interworks with the conventional system and can switch to 2D or 3D service according to the broadcast programming and schedule. The system also provides a mechanism that can prevent the synchronization mismatch between left and right video streams and between the stream and the associated signaling in the 2D/3D transition periods. The picture quality measurements are carried out based on the ITU-R recommended test to check the level of quality of service provided by the proposed scheme. The conformity tests are also performed with the conventional channel and the receiver for the DTV system to confirm the feasibility of the proposed one for the commercial service.

Searchable encryption systems provide search on encrypted data while preserving the privacy of the data and the search keywords used in queries. Recently, interest on data outsourcin g has increased due to proliferation of cloud computing service s. Many researches are on going to minimize the trust put on exter nal servers and searchable encryption is one of them. However, most of previous searchable encryption schemes provide only a single keyword boolean search. Although, there have been proposals to provide conjunctive keyword search, most of t hese works use a fixed field which limit their application. In this paper, we propose a field-free conjunctive keyword searcha ble encryption that also provides rank information of search results. Our system uses prime tables and greatest common divisor operation, making our system very efficient. Moreover, our system is practical and can be implemented very easily since it does not require sophisticated cryptographic module. Keywords: searchable encryption, conjunctive keyword search, rank, prim e table접수일(2013년 7월 5일), 수정일(2013년 11월 19일), 게재확정일(2013년 12월 3일)†주저자, sunyujin@koreatech.ac.kr‡교신저자, sangjin@koreatech.ac.kr (Corresponding author)

Sanghun LEE*, Takao ENDO*, Toshiro KAMADA** and Sangjin KIM*** *Department of Civil and Environmental Engineering, Tohoku Gakuin University, Miyagi 985-8537, Japan E-mail : leesh@mail.tohoku-gakuin.ac.jp **Department of Civil Engineering, Graduate School of Engineering, Osaka University, Osaka 565-0871, Japan ***Department of Civil Engineering, College of Ocean Sciences, Jeju National University, Jeju 690-756, Korea

CAS used in IPTV or DTV has an environment of sending single type of contents through single streaming. But it can be improved to support users’ various video applications through single streaming by combining with SVC. For such an environment, efficiency should be firstly considered, and hierarchical key management methods for billing policy by service levels should be applied. This study aims to look into considerations to apply SVC to CAS and propose SVC encryption in CAS environment. The security of the proposed scheme is based on the safety of CAS and oneway hash function. If the proposed scheme is applied, scalability can be efficiently provided even in the encrypted contents and it is possible to bill users according to picture quality. In addition, the test resul ts show that SVC contents given by streaming service with the average less than 10%overhead can be safely protected against illegal uses.

An energy-efficient maximum power point tracking (MPPT) circuit with a fast-tracking time for use with 800-μW PV energy harvesters is presented in this paper. The proposed MPPT circuit uses a successive approximation register MPPT algorithm, which has a power down mode and a fast tracking time, to achieve low power consumption and energy savings. The prototype MPPT circuit, which consists of analog-based circuits, has been implemented and fabricated in a 0.35-μm BCDiMOS process. The MPPT core occupies an area of 3 mm2 and consumes 4.6 μW of power. The tracking time is reduced by 69.4% and the stored energy is increased by 31.4% as compared to the conventional hill climbing-based MPPT algorithm under indoor conditions.

Proxy re-encryption scheme has advantage where plaintext does not get exposed during re-encryption process. This scheme is popular for sharing server-saved data in case of cloud computing or mobile office that uses server to save data. Since previous proxy re-encryption schemes can use re-encryption key over and over again, it may abuse re-encryption. To solve this problem, conditional proxy re-encryption scheme was proposed. But, it is computationally expensive generate the same number of re-encryption key with the number of condition values. In this paper, we propose an efficient conditional proxy re-encryption scheme in terms of re-encryption key generation. The proposed scheme uses only encryption and decryption process. Therefore it has advantage to generate one re-encryption key for one person. The proposed scheme is secure against chosen-ciphertext attack.

Comparable encryption allows a verifier to test whether given two ciphertexts from a probabilistic public key cryptosystem are encryption of the same message without decrypting them. Recently, Yang et al. proposed such scheme and Lee et al. and Tang independently modified Yang et al.`s system to restrict the entity who can perform the verification. However, the original Yang et al.`s scheme has a flaw that enables two ciphertexts which are not encryption of the same message to pass the test. In this paper, we concretely show the faults in all three schemes considered and analyze the effect of this flaw in the use of such schemes in applications.

This letter presents a novel contrast enhancement approach based on dominant brightness level analysis and adaptive intensity transformation for remote sensing images. The proposed algorithm computes brightness-adaptive intensity transfer functions using the low-frequency luminance component in the wavelet domain and transforms intensity values according to the transfer function. More specifically, we first perform discrete wavelet transform (DWT) on the input images and then decompose the LL subband into low-, middle-, and high-intensity layers using the log-average luminance. Intensity transfer functions are adaptively estimated by using the knee transfer function and the gamma adjustment function based on the dominant brightness level of each layer. After the intensity transformation, the resulting enhanced image is obtained by using the inverse DWT. Although various histogram equalization approaches have been proposed in the literature, they tend to degrade the overall image quality by exhibiting saturation artifacts in both low- and high-intensity regions. The proposed algorithm overcomes this problem using the adaptive intensity transfer function. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than existing techniques. The proposed method can effectively enhance any low-contrast images acquired by a satellite camera and is also suitable for other various imaging devices such as consumer digital cameras, photorealistic 3-D reconstruction systems, and computational cameras.

Recently, a new type of Radio Frequency IDentification (RFID) system with mobile readers is introduced. In such a system, it is more desirable for mobile readers to identify tags without a back-end server, and thus it is frequently referred as a serverless mobile RFID system. In this paper, we formalize a serverless mobile RFID system model and propose a new encryption-based system that preserves the privacy of both tags and readers in the model. In addition, we define a new adversary model for the system model and show the security of the proposed system. Throughout comparisons between ours and the other alternatives, we show that our proposed system provides a stronger reader privacy and robustness against a reader forgery attack than the competitors.

RSA-CRT is one of the commonly used techniques to speedup RSA operation. Since RSA-CRT performs its operations based on the modulus of two private primes, it is about four times faster than RSA. In RSA, the two primes are normally thrown away after generating the public key pair. However, in RSA-CRT, the two primes are directly used in RSA operations. This led to hardware fault attacks which can be used to factor the public modulus. The most common way to counter these attacks is based on error propagation. In these schemes, all the outputs of RSA are affected by the infected error which makes it difficult for an adversary to use the output to factor the public modulus. However, the error propagation has sequentialized the RSA operation. Moreover, these schemes have been found to be still vulnerable to hardware fault attacks. In this paper, we propose two new RSA-CRT schemes which are both resistant to hardware fault attack and support parallel execution: one uses common modulus and the other one perform operations in each prime modulus. Both proposed schemes takes about a time equal to two exponentiations to complete the RSA operation if parallel execution is fully used and can protect the two private primes from hardware fault attacks.

Image restoration plays a major role in improving image quality as a preprocessing step in various imaging systems. While conventional image restoration techniques, such as the constrained least-squares (CLS) and the Wiener filters, often exhibit either noise amplification or over-smoothing problem. On the other hand, advanced image restoration techniques, such as the iterative regularization and the combined Fourier and wavelet domain thresholding filters, are not suitable for real-time applications because of high computational overhead. For overcoming these problems, we present a novel vaguelette-wavelet decomposition (VWD) approach to real-time, space-frequency-adaptive image restoration based on frequency-adaptive shrinkage and directional wavelet bases. The proposed algorithm effectively reduces noise by adaptively shrinking wavelet coefficients based on alpha map and entropy, and restores a degraded image using the vaguelette-wavelet transform. In generating vaguelettes, we use the estimated point-spread function (PSF) and new directional wavelet bases. The extended set of experimental results confirm that the proposed algorithm can restore sharp details without amplifying noise, and is suitable for commercial low-cost, high-quality imaging devices, such as digital and computational cameras, visual surveillance systems, and consumer's camcorders.

This paper presents a novel wavelet-domain color image enhancement using filtered directional bases and frequency-adaptive shrinkage. Most traditional noise reduction methods tend to over-suppress high-frequency details. For overcoming this problem we first decompose the input image into flat and edge regions, and remove noise using the alpha map computed from wavelet transform coefficients of LH, HL, and HH bands. After removing noise in the flat region, we further remove noise in edge regions by adaptively shrinking wavelet coefficients based on the entropy. Moreover, we present a new directional transform using wavelet basis and Gaussian low pass filters. The wavelet coefficients of edge regions are inverse transformed by using the filtered wavelet bases. Experimental results show the proposed algorithm can reduce noise without losing sharp details and is suitable for commercial low-cost imaging systems, such as digital cameras, CCTV, and surveillance system.

In this paper, we present a novel image enhancement approach using a color shift model-based multiple color-filter aperture (MCA) camera for digital multifocusing. The proposed image enhancement algorithm consists of three steps; (i) cluster-based region-of-interest (ROI) estimation, (ii) image registration using phase correlation matching (PCM) and fusion, and (iii) image enhancement using spatially adaptive noise smoothing based on the alpha map. The image acquired by the MCA configuration contains color misalignment, which provides additional depth information of objects at different distances. This color misalignment can also provide additional information for blur estimation. The proposed cluster-based image segmentation method can effectively classify ROIs according to the distance from the camera. The segmented regions are aligned by using PCM, and they are fused to generate an in-focused image. For further enhancement of the color-registered image, we use spatially adaptive noise smoothing based on the alpha map. Experimental results show the proposed image enhancement method can significantly enhance the visual quality of the MCA output image, and can be fully or partially incorporated into multifocusing or extended depth of field (EDoF) systems in the form of the finite impulse response (FIR) filter structure.

In this paper, we present a novel real-time image restoration approach using a truncated constrained least-squares (TCLS) filter and spatially adaptive noise smoothing (SANS) algorithm based on alpha map for the extended depth of field (EDoF) system in an image signal processing (ISP) chain. The proposed TCLS filter and the alpha map-based SANS algorithm can be implemented in the Bayer-domain by using a general finite impulse response (FIR) structure. The TCLS filter coefficients are priori determined according to the given point-spread-function (PSF) by optical lens simulation, and deconvolution is performed in the Bayer-domain instead of the RGB-domain. The SANS algorithm can successfully remove noise in flat regions without affecting the sharply restored details. Based on the extended set of experimental results the proposed algorithm is proved to be able to restore an accurately focused image in real-time, and is suitable for commercial low-cost, high-quality imaging devices such as a digital camera and a camcorder.

In an RFID search protocol, a reader uses a designated query instead of an unspecified query commonly used in RFID authentication protocols. Due to this fundamental difference, techniques used in RFID authentication protocols may not be suitable for RFID search protocols. Tan et al.'s protocol, however, is based on techniques used in previous works such as using random values. In this paper, we propose two RFID search protocols, one based on static ID and the other based on dynamic ID, both which does not require additional measures to satisfy security requirements of RFID protocols. We achieve this by using counters.

Background.The iterative sure independence screening (ISIS) is a popular method in selecting important variables while maintaining most of the informative variables relevant to the outcome in high throughput data. However, it not only is computationally intensive but also may cause high false discovery rate (FDR). We propose to use the FDR as a screening method to reduce the high dimension to a lower dimension as well as controlling the FDR with three popular variable selection methods: LASSO, SCAD, and MCP.Method.The three methods with the proposed screenings were applied to prostate cancer data with presence of metastasis as the outcome.Results.Simulations showed that the three variable selection methods with the proposed screenings controlled the predefined FDR and produced high area under the receiver operating characteristic curve (AUROC) scores. In applying these methods to the prostate cancer example, LASSO and MCP selected 12 and 8 genes and produced AUROC scores of 0.746 and 0.764, respectively.Conclusions. We demonstrated that the variable selection methods with the sequential use of FDR and ISIS not only controlled the predefined FDR in the final models but also had relatively high AUROC scores.

This paper presents the field trial results of the terrestrial digital multimedia broadcasting (T-DMB) service in Korea. The T-DMB system, based on the Eureka-147 digital audio broadcasting (DAB) system, provides streaming video, CD-quality audio and data services at speeds of up to 300 km/h using H.264 video codec along with additional channel coding and interleaving schemes. In order to provide seamless high-quality services on various radio channels, we measured the bit error rate (BER), electric field strength (EFS), and subjective video quality using specially-designed mobile test equipment. From the field tests we obtained the required effective EFS (EEFS) for error-free reception and also derived the minimum EFS from the link budget analysis. Furthermore, we present the optimal transmit and receive parameters for constructing the T-DMB networks and also developing T-DMB broadcasting equipment and mobile receivers

In this paper, we investigate a combination of several feedforward deep neural networks (DNNs) for a high-quality statistical parametric speech synthesis system. Recently, DNNs have significantly improved the performance of essential components in the statistical parametric speech synthesis, e.g. spectral feature extraction, acoustic modeling and spectral post-filter. In this paper our proposed technique combines these feed-forward DNNs so that the DNNs can perform all standard steps of the statistical speech synthesis from end to end, including the feature extraction from STRAIGHT spectral amplitudes, acoustic modeling, smooth trajectory generation and spectral post-filter.The proposed DNN-based speech synthesis system is then compared to the state-of-the-art speech synthesis systems, i.e. conventional HMM-based, DNN-based and unit selection ones.

This paper presents a feature-based object tracking algorithm using optical flow under the non-prior training (NPT) active feature model (AFM) framework. The proposed tracking procedure can be divided into three steps: (i) localization of an object-of-interest, (ii) prediction and correction of the object's position by utilizing spatio-temporal information, and (iii) restoration of occlusion using NPT-AFM. The proposed algorithm can track both rigid and deformable objects, and is robust against the object's sudden motion because both a feature point and the corresponding motion direction are tracked at the same time. Tracking performance is not degraded even with complicated background because feature points inside an object are completely separated from background. Finally, the AFM enables stable tracking of occluded objects with maximum 60% occlusion. NPT-AFM, which is one of the major contributions of this paper, removes the off-line, preprocessing step for generating a priori training set. The training set used for model fitting can be updated at each frame to make more robust object's features under occluded situation. The proposed AFM can track deformable, partially occluded objects by using the greatly reduced number of feature points rather than taking entire shapes in the existing shape-based methods. The on-line updating of the training set and reducing the number of feature points can realize a real-time, robust tracking system. Experiments have been performed using several in-house video clips of a static camera including objects such as a robot moving on a floor and people walking both indoor and outdoor. In order to show the performance of the proposed tracking algorithm, some experiments have been performed under noisy and low-contrast environment. For more objective comparison, PETS 2001 and PETS 2002 datasets were also used.

The distribution of a air pollutant which is emitted from the building materials in a room can be quantitatively predicted by the CFD (Computational Fluid Dynamics) technique. The authors develop a CFD analysis incorporating a pollutant sorption model which allows the detailed analysis of the effect of reducing the concentration of the indoor air pollutant through the sorption on walls and other interior surfaces.In order to demonstrate the usefulness of the CFD analysis incorporating the sorption model, we conduct an analysis of concentration distribution for an indoor air pollutant in a closed heated room. We examine the effect of reducing the concentration of the indoor air pollutant by installing an adsorber in the walls and ceiling, assuming that the pollutant was emitted from the floor of the room. The sorption model incorporated is very simple and assumes zero concentration at the adsorbing surfaces. The CFD analysis shows the significant effect of reducing the concentration of the indoor air pollutant by the absorbing surfaces.

This paper presents a new online check system that solves the reusability problem of refunds in existing systems using the partially blind signature. The clear part of the signature is used to encode the face value of a check. In our system, refunds can be reused in payment in the same way as withdrawn checks without any limitation. We also use a one-time secret key as the serial number of a check to increase the efficiency of payment. The new system provides multiple offline shopping sessions to minimize the number of online messages. During the offline session, we use a one-way accumulator to construct a proof of payment. The security and the atomicity of the system is also discussed.

In this article, we present a hybrid render farm which can accommodate cloud-based rendering servers to extend the rendering capacity of a local cluster render farm. A rendering workload in real world tends to highly fluctuate so a seamless execution environment for overloaded jobs is a strong demand from post-production. However, running a rendering job on a remote cloud has a few challenging issues: input data synchronization and complex infrastructure management. Our proposed Render Verse can overcome mentioned hurdles with an application-aware caching scheme coupled with a repository and a hierarchical server node management.

The paper deals with analytical and experimental study to determine the cause of transverse crack patterns on two parallel seven-span haunched steel-girder bridges. Major cracks are concentrated between the beginning and the middle of the third span. The crack patterns are symmetric with respect to the fourth span and can be found on both bridges. Diagnostic tests were performed with crawling-speed and high-speed trucks. The measured distribution factors are consistently more uniform than code-specified values and the impact factors are smaller than specified values. Tests on concrete cores indicated that the water/cement ratio is higher than code specified ratio and that the strength of concrete is greater than specified. Three-dimensional finite-element analysis indicated that stresses in the slab due to live load do not match with existing crack patterns. However, the analysis of the concrete pouring sequence showed that maximum concrete stresses are large and the locations correspond to those of the most severe slab cracking. Therefore, it is concluded that the bridge deck slab cracks resulted primarily from the deck pouring sequence, combined with concrete shrinkage. Since the primary causes of cracks are not active anymore, an epoxy injection method has been recommended to seal cracks.

The rapid growth of computational wind engineering (CWE) has led to an expansion of the research fields of wind engineering. CWE has made it possible to analyze various physical processes associated with wind climate around humans and in urban areas. This paper reviews recent achievements in CWE and its application to wind climate in scales ranging from human to urban scale.

Wind velocity and temperature fields over Kobe city which experienced a large-scale fire are numerically simulated by using CFD (Computational Fluid Dynamics) technique based on a turbulence model. Two cases of numerical simulations are studied for a typical meteorological condition of winter; one is with fire and the other is without fire. The result shows that the characteristics of wind velocity field over Kobe city are well predicted in both cases. In the case with fire, the horizontal wind converging is clearly obtained at the center of the fire. Also, the vertical circulation of wind flow caused by the fire plume is well observed over the city.