In this paper we analyze the interval algorithm for random number generation proposed by Han and Hoshi in the case of Markov coin tossing. Using the expression of real numbers on the interval [0,1), we first establish an explicit representation of the interval algorithm with the representation of real numbers on the interval [0,1) based one number systems. Next, using the expression of the interval algorithm, we give a rigorous analysis of the interval algorithm. We discuss the difference between the expected number of the coin tosses in the interval algorithm and their upper bound derived by Han and Hoshi and show that it can be characterized explicitly with the established expression of the interval algorithm.

A spatially “Mt. Fuji” coupled (SFC) low-density parity-check (LDPC) ensemble is a modified version of the spatially coupled (SC) LDPC ensemble. Its decoding error probability in the waterfall region has been studied only in an experimental manner. In this paper, we theoretically analyze it over the binary erasure channel by modifying the expected graph evolution (EGE) and covariance evolution (CE) that have been used to analyze the original SC-LDPC ensemble. In particular, we derive the initial condition modified for the SFC-LDPC ensemble. Then, unlike the SC-LDPC ensemble, the SFC-LDPC ensemble has a local minimum on the solution of the EGE and CE. Considering the property of it, we theoretically expect the waterfall curve of the SFC-LDPC ensemble is steeper than that of the SC-LDPC ensemble. In addition, we also confirm it by numerical experiments.

The network load is increasing due to the spread of content distribution services. Caching is recognized as a technique to reduce the peak network load by storing popular content into memories of users. Coded caching is a new caching approach based on a carefully designed content placement to create coded multicasting opportunities. Coded caching schemes in single-rate networks are evaluated by the tradeoff between the size of memory and that of delivered data. For considering the network with multiple transmission rates, it is crucial how to operate multicast. In multicast delivery, a sender must communicate to intended receivers at a rate that is available to all receivers. Multicast scheduling method of determining rates to deliver are evaluated by throughput and delay in multi-rate wireless networks. In this paper, we discuss coded caching in the multi-rate wireless networks. We newly define a measure for evaluating the coded caching scheme as coded caching delay and propose a new coded caching scheme. Also, we compare the proposed coded caching scheme with conventional coded caching schemes and show that the proposed scheme is suitable for multi-rate wireless networks.

Pilot contamination is addressed in massive multiple-input multiple-output (MIMO) uplink. The main ideas of pilot decontamination are twofold: One is to design transmission timing of pilot sequences such that the pilot transmission periods in different cells do not fully overlap with each other, as considered in previous works. The other is joint channel and data estimation via approximate message-passing (AMP) for bilinear inference. The convergence property of conventional AMP is bad in bilinear inference problems, so that adaptive damping was required to help conventional AMP converge. The main contribution of this paper is a modification of the update rules in conventional AMP to improve the convergence property of AMP. Numerical simulations show that the proposed AMP outperforms conventional AMP in terms of estimation performance when adaptive damping is not used. Furthermore, it achieves better performance than state-of-the-art methods based on subspace estimation when the power difference between cells is small.

Machine learning models inherently memorize significant amounts of information, and thus hiding not only prediction processes but also trained models, i.e., model obliviousness, is desirable in the cloud setting. Several works achieved model obliviousness with the MNIST dataset, but datasets that include complicated samples, e.g., CIFAR-10 and CIFAR-100, are also used in actual applications, such as face recognition. Secret sharing-based secure prediction for CIFAR-10 is difficult to achieve. When a deep layer architecture such as CNN is used, the calculation error when performing secret calculation becomes large and the accuracy deteriorates. In addition, if detailed calculations are performed to improve accuracy, a large amount of calculation is required. Therefore, even if the conventional method is applied to CNN as it is, good results as described in the paper cannot be obtained. In this paper, we propose two approaches to solve this problem. Firstly, we propose a new protocol named Batch-normalizedActivation that combines BatchNormalization and Activation. Since BatchNormalization includes real number operations, when performing secret calculation, parameters must be converted into integers, which causes a calculation error and decrease accuracy. By using our protocol, calculation errors can be eliminated, and accuracy degradation can be eliminated. Further, the processing is simplified, and the amount of calculation is reduced. Secondly, we explore a secret computation friendly and high accuracy architecture. Related works use a low-accuracy, simple architecture, but in reality, a high accuracy architecture should be used. Therefore, we also explored a high accuracy architecture for the CIFAR10 dataset. Our proposed protocol can compute prediction of CIFAR-10 within 15.05 seconds with 87.36% accuracy while providing model obliviousness.

To enhance the user's privacy in electronic ID, anonymous credential systems have been researched. In the anonymous credential system, a trusted issuing organization first issues a certificate certifying the user's attributes to a user. Then, in addition to the possession of the certificate, the user can anonymously prove only the necessary attributes. Previously, an anonymous credential system was proposed, where CNF (Conjunctive Normal Form) formulas on attributes can be proved. The advantage is that the attribute proof in the authentication has the constant size for the number of attributes that the user owns and the size of the proved formula. Thus, various expressive logical relations on attributes can be efficiently verified. However, the previous system has a limitation: The proved CNF formulas cannot include any negation. Therefore, in this paper, we propose an anonymous credential system with constant-size attribute proofs such that the user can prove CNF formulas with negations. For the proposed system, we extend the previous accumulator for the limited CNF formulas to verify CNF formulas with negations.

Change-point detection is the problem of finding points of time when a probability distribution of samples changed. There are various related problems, such as estimating the number of the change-points and estimating magnitude of the change. Though various statistical models have been assumed in the field of change-point detection, we particularly deal with i.p.i.d. (independent-piecewise-identically-distributed) sources. In this paper, we formulate the related problems in a general manner based on statistical decision theory. Then we derive optimal estimators for the problems under the Bayes risk principle. We also propose efficient algorithms for the change-point detection-related problems in the i.p.i.d. sources, while in general, the optimal estimations requires huge amount of calculation in Bayesian setting. Comparison of the proposed algorithm and previous methods are made through numerical examples.

A quadratic extension field (QEF) defined by F₁ = F_p[α]/(α²+1) is typically used for a supersingular isogeny Diffie-Hellman (SIDH). However, there exist other attractive QEFs F_i that result in a competitive or rather efficient performing the SIDH comparing with that of F₁. To exploit these QEFs without a time-consuming computation of the initial setting, the authors propose to convert existing parameter sets defined over F₁ to F_i by using an isomorphic map F₁ → F_i.

In this paper, a balance operation Transimpedance Amplifier (TIA) with low-noise has been implemented for optical receivers in 130 nm SiGe BiCMOS Technology, in which the optimal tradeoff emitter current density and the location of high-frequency noise corner were analyzed for acquiring low-noise performance. The Auto-Zero Feedback Loop (AZFL) without introducing unnecessary noises at input of the TIA, the tail current sink with high symmetries and the balance operation TIA with the shared output of Operational Amplifier (OpAmp) in AZFL were designed to keep balanced operation for the TIA. Moreover, cascode and shunt-feedback were also employed to expanding bandwidth and decreasing input referred noise. Besides, the formula for calculating high-frequency noise corner in Heterojunction Bipolar Transistor (HBT) TIA with shunt-feedback was derived. The electrical measurement was performed to validate the notions described in this work, appearing 9.6 pA/√Hz of input referred noise current Power Spectral Density (PSD), balance operation (V_IN1=896mV, V_IN2=896mV, V_OUT1=1.978V, V_OUT2=1.979V), bandwidth of 32GHz, overall transimpedance gain of 68.6dBΩ, a total 117mW power consumption and chip area of 484µm × 486µm.

In this paper, we propose four optimization methods during the Register Transfer Level (RTL) conversion from synchronous RTL models into asynchronous RTL models. The modularization of data-path resources and the use of appropriate D flip-flops reduce the circuit area. Fixing the control signal of the multiplexers and inserting latches for the data-path resources reduce the dynamic power consumption. In the experiment, we evaluated the effect of the proposed optimization methods. The combination of all optimization methods could reduce the energy consumption by 21.9% on average compared to the ones without the proposed optimization methods.

In this paper, we propose a design method to design asynchronous circuits with bundled-data implementation on commercial Field Programmable Gate Arrays using placement constraints. The proposed method uses two types of placement constraints to reduce the number of delay adjustments to fix timing violations and to improve the performance of the bundled-data implementation. We also propose a floorplan algorithm to reduce the control-path delays specific to the bundled-data implementation. Using the proposed method, we could design the asynchronous circuits whose performance is close to and energy consumption is small compared to the synchronous counterparts with less delay adjustment.

High level synthesis (HLS) is a source-code-driven Register Transfer Level (RTL) design tool, and the performance, the power consumption, and the area of a generated RTL are limited partly by the description of a HLS input source code. In order to break through such kind of limitation and to get a further optimized RTL, the optimization of the input source code is indispensable. Routing congestion is one of such problems we need to consider the refinement of a HLS input source code. In this paper, we propose a novel HLS flow that performs code improvements by detecting congested parts directly from HLS input source code without using physical logic synthesis, and regenerating the input source code for HLS. In our approach, the origin of the wire congestion is detected from the HLS input source code by applying pattern matching on Program-Dependence Graph (PDG) constructed from the HLS input source code, the possibility of wire congestion is reported.

FPGA that exploits via-switches, which are a kind of non-volatile resistive RAMs, for crossbar implementation is attracting attention due to its high integration density and energy efficiency. Via-switch crossbar is responsible for the signal routing in the interconnections by changing on/off-states of via-switches. To verify the via-switch crossbar functionality after manufacturing, fault testing that checks whether we can turn on/off via-switches normally is essential. This paper confirms that a general differential pair comparator successfully discriminates on/off-states of via-switches, and clarifies fault modes of a via-switch by transistor-level SPICE simulation that injects stuck-on/off faults to atom switch and varistor, where a via-switch consists of two atom switches and two varistors. We then propose a fault diagnosis methodology for via-switches in the crossbar that diagnoses the fault modes according to the comparator response difference between the normal and faulty via-switches. The proposed method achieves 100% fault detection by checking the comparator responses after turning on/off the via-switch. In case that the number of faulty components in a via-switch is one, the ratio of the fault diagnosis, which exactly identifies the faulty varistor and atom switch inside the faulty via-switch, is 100%, and in case of up to two faults, the fault diagnosis ratio is 79%.

In this paper, we aim to develop technologies for the circuit fault diagnosis and propose a formulation of a measure of a test pattern for the circuit fault diagnosis. Given a faulty circuit, the fault diagnosis is to deduce locations of faults that had occurred in the circuit. The fault diagnosis is executed in software before the failure analysis by which engineers inspect physical defects, and helps to improve the manufacturing process which yielded faulty circuits. The heart of the fault diagnosis is to distinguish between candidate faults by using test patterns, which are applied to the circuit-under-diagnosis (CUD), and thus test patterns that can distinguish as many faults as possible need to be generated. This fact motivates us to consider the test pattern measure based on the number of fault-pairs that become distinguished by a test pattern. To the best of the authors' knowledge, that measure requires the computational time of complexity order O(N_F²), where N_F denotes the number of candidate faults. Since N_F is generally large for real industrial circuits, the computational time of the measure is long even when a high-performance computer is used. The formulation proposed in this paper makes it possible to calculate the measure in the computational complexity of O(N_F log N_F), and thus that measure is useful for the test pattern selection in the fault diagnosis. In computational experiments, the effectiveness of the formulation is demonstrated as samples of computational times of the measure calculated by the traditional and the proposed formulae and thorough comparisons between several greedy heuristics which are based on the measure.

Stochastic computing (SC), which is an approximate computation with probabilities, has attracted attention owing to its small area, small power consumption and high fault tolerance. In this paper, we focus on the transient fault tolerance of SC based on linear finite state machines (linear FSMs). We show that state assignment of FSMs considerably affects the fault tolerance of linear FSM-based SC circuits, and present a Markov model for representing the impact of the state assignment on the behavior of faulty FSMs and estimating the expected error significance of the faulty FSM-based SC circuits. Furthermore, we propose a heuristic algorithm for appropriate state assignment that can mitigate the influence of transient faults. Experimental analysis shows that the state assignment has an impact on the transient fault tolerance of linear FSM-based SC circuits and the proposed state assignment algorithm can achieve a quasi-optimal state assignment in terms of high fault tolerance.

This paper shows design and implementation of an image-based vibration detection system on a field-programmable gate array (FPGA), aiming at application to tremor suppression for microsurgery assistance systems. The system can extract a vibration component within a user-specified frequency band from moving images in real-time. For fast and robust detection, we employ a statistical approach using dense optical flow to derive vibration component, and design a custom hardware based on the Lucas-Kanade (LK) method to compute optical flow. And for band-pass filtering without phase delay, we implement the band-limited multiple Fourier linear combiner (BMFLC), a sort of adaptive band-pass filter which can recompose an input signal as a mixture of sinusoidal signals with multiple frequencies within the specified band, with no phase delay. The whole system is implemented as a deep pipeline on a Xilinx Kintex-7 XC7K325T FPGA without using any external memory. We employ fixed-point arithmetic to reduce resource utilization while maintaining accuracy close to double-precision floating-point arithmetic. Empirical experiments reveal that the proposed system extracts a high-frequency tremor component from hand motions, with intentional low-frequency motions successfully filtered out. The system can process VGA moving images at 60fps, with a delay of less than 1 µs for the BMFLC, suggesting effectiveness of the deep pipelined architecture. In addition, we are planning to integrate a CNN-based segmentation system for improving detection accuracy, and show preliminary software evaluation results.

In this paper, we propose a WiFi-based indoor positioning system using a fingerprint method, whose database is constructed with estimated reference locations. The reference locations and their information, called data sets in this paper, are obtained by moving reference devices at a constant speed while gathering information of available access points (APs). In this approach, the reference locations can be estimated using the velocity without any precise reference location information. Therefore, the cost of database construction can be dramatically reduced. However, each data set includes some errors due to such as the fluctuation of received signal strength indicator (RSSI) values, the device-specific WiFi sensitivities, the AP installations, and removals. In this paper, we propose a method to merge data sets to construct a consistent database suppressing such undesired effects. The proposed approach assumes that the intervals of reference locations in the database are constant and that the fingerprint for each reference location is calculated from multiple data sets. Through experimental results, we reveal that our approach can achieve an accuracy of 80%. We also show a detailed discussion on the results related parameters in the proposed approach.

In this paper, we propose a method which enables us to control the variance of the coefficients of the LMS-type adaptive filters. In the method, each coefficient of the adaptive filter is modeled as an random variable with a Gaussian distribution, and its value is estimated as the mean value of the distribution. Besides, at each time, we check if the updated value exists within the predefined range of distribution. The update of a coefficient will be canceled when its updated value exceeds the range. We propose an implementation method which has similar formula as the Gaussian mixture model (GMM) widely used in signal processing and machine learning. The effectiveness of the proposed method is evaluated by the computer simulations.

The spike height of volleyball players is important in volleyball analysis as the quantitative criteria to evaluation players' motions, which not only provides rich information to audiences in live broadcast of sports events but also makes contribution to evaluate and improve the performance of players in strategy analysis and players training. In the volleyball game scene, the high similarity between hands, the deformation and the occlusion are three main problems that influence the acquisition performance of spike height. To solve these problems, this paper proposes a body part connection, categorization and occlusion based observation model and a temporal position based correction method. Firstly, skin pixel filter based connection detection solves the problem of high similarity between hands by judging whether a hand is connected to the spike player. Secondly, the body part categorization based observation uses the probability distribution map of hand to determine the category of each body part to solve the deformation problem. Thirdly, the occlusion part detection based observation eliminates the influence of the views with occluded body part by detecting the occluded views with a trained classifier of body part. At last, the temporal position based result correction combines the estimated results, which refers the historical positions, and the posterior result to obtain an optimal result by degree of confidence. The experiments are based on the videos of final and semi-final games of 2014 Japan Inter High School Men's Volleyball in Tokyo Metropolitan Gymnasium, which includes 196 spike sequences of 4 teams. The experiment results of proposed methods are that: 93.37% of test sequences can be successfully detected the spike height, and in which the average error of spike height is 5.96cm.

Recently, visual trackers based on the framework of kernelized correlation filter (KCF) achieve the robustness and accuracy results. These trackers need to learn information on the object from each frame, thus the state change of the object affects the tracking performances. In order to deal with the state change, we propose a novel KCF tracker using the filter response map, namely a confidence map, and adaptive model. This method firstly takes a skipped scale pool method which utilizes variable window size at every two frames. Secondly, the location of the object is estimated using the combination of the filter response and the similarity of the luminance histogram at multiple points in the confidence map. Moreover, we use the re-detection of the multiple peaks of the confidence map to prevent the target drift and reduce the influence of illumination. Thirdly, the learning rate to obtain the model of the object is adjusted, using the filter response and the similarity of the luminance histogram, considering the state of the object. Experimentally, the proposed tracker (CFCA) achieves outstanding performance for the challenging benchmark sequence (OTB2013 and OTB2015).

Motion deblurring for noisy and blurry images is an arduous and fundamental problem in image processing community. The problem is ill-posed as many different pairs of latent image and blur kernel can render the same blurred image, and thus, the optimization of this problem is still unsolved. To tackle it, we present an effective motion deblurring method for noisy and blurry images based on prominent structure and a data-driven heavy-tailed prior of enhanced gradient. Specifically, first, we employ denoising as a preprocess to remove the input image noise, and then restore strong edges for accurate kernel estimation. The image extreme channels-based priors (dark channel prior and bright channel prior) as sparse complementary knowledge are exploited to extract prominent structure. High closeness of the extracted structure to the clear image structure can be obtained via tuning the parameters of extraction function. Next, the integration term of enhanced interim image gradient and clear image heavy-tailed prior is proposed and then embedded into the image restoration model, which favors sharp images over blurry ones. A large number of experiments on both synthetic and real-life images verify the superiority of the proposed method over state-of-the-art algorithms, both qualitatively and quantitatively.

In this paper, we address the problem of analyzing topics, included in a social video group, to improve the retrieval performance of videos. Unlike previous methods that focused on an individual visual aspect of videos, the proposed method aims to leverage the “mutual reinforcement” of heterogeneous modalities such as tags and users associated with video on the Internet. To represent multiple types of relationships between each heterogeneous modality, the proposed method constructs three subgraphs: user-tag, video-video, and video-tag graphs. We combine the three types of graphs to obtain a heterogeneous graph. Then the extraction of latent features, i.e., topics, becomes feasible by applying graph-based soft clustering to the heterogeneous graph. By estimating the membership of each grouped cluster for each video, the proposed method defines a new video similarity measure. Since the understanding of video content is enhanced by exploiting latent features obtained from different types of data that complement each other, the performance of visual reranking is improved by the proposed method. Results of experiments on a video dataset that consists of YouTube-8M videos show the effectiveness of the proposed method, which achieves a 24.3% improvement in terms of the mean normalized discounted cumulative gain in a search ranking task compared with the baseline method.

An inpainting method via sparse representation based on a new phaseless quality metric is presented in this paper. Since power spectra, phaseless features, of local regions within images enable more successful representation of their texture characteristics compared to their pixel values, a new quality metric based on these phaseless features is newly derived for image representation. Specifically, the proposed method enables spare representation of target signals, i.e., target patches, including missing intensities by monitoring errors converged by phase retrieval as the novel phaseless quality metric. This is the main contribution of our study. In this approach, the phase retrieval algorithm used in our method has the following two important roles: (1) derivation of the new quality metric that can be derived even for images including missing intensities and (2) conversion of phaseless features, i.e., power spectra, to pixel values, i.e., intensities. Therefore, the above novel approach solves the existing problem of not being able to use better features or better quality metrics for inpainting. Results of experiments showed that the proposed method using sparse representation based on the new phaseless quality metric outperforms previously reported methods that directly use pixel values for inpainting.

Designing an optimum quantizer can be treated as the optimization problem of finding the quantization indices that minimize the quantization error. One solution to the optimization problem, DP quantization, is based on dynamic programming. Some applications, such as bit-depth scalable codec and tone mapping, require the construction of multiple quantizers with different quantization levels, for example, from 12bit/channel to 10bit/channel and 8bit/channel. Unfortunately, the above mentioned DP quantization optimizes the quantizer for just one quantization level. That is, it is unable to simultaneously optimize multiple quantizers. Therefore, when DP quantization is used to design multiple quantizers, there are many redundant computations in the optimization process. This paper proposes an extended DP quantization with a complexity reduction algorithm for the optimal design of multiple quantizers. Experiments show that the proposed algorithm reduces complexity by 20.8%, on average, compared to conventional DP quantization.

We propose a novel hue-correction scheme for multi-exposure image fusion (MEF). Various MEF methods have so far been studied to generate higher-quality images. However, there are few MEF methods considering hue distortion unlike other fields of image processing, due to a lack of a reference image that has correct hue. In the proposed scheme, we generate an HDR image as a reference for hue correction, from input multi-exposure images. After that, hue distortion in images fused by an MEF method is removed by using hue information of the HDR one, on the basis of the constant-hue plane in the RGB color space. In simulations, the proposed scheme is demonstrated to be effective to correct hue-distortion caused by conventional MEF methods. Experimental results also show that the proposed scheme can generate high-quality images, regardless of exposure conditions of input multi-exposure images.

We propose a privacy-preserving machine learning scheme with encryption-then-compression (EtC) images, where EtC images are images encrypted by using a block-based encryption method proposed for EtC systems with JPEG compression. In this paper, a novel property of EtC images is first discussed, although EtC ones was already shown to be compressible as a property. The novel property allows us to directly apply EtC images to machine learning algorithms non-specialized for computing encrypted data. In addition, the proposed scheme is demonstrated to provide no degradation in the performance of some typical machine learning algorithms including the support vector machine algorithm with kernel trick and random forests under the use of z-score normalization. A number of facial recognition experiments with are carried out to confirm the effectiveness of the proposed scheme.

We propose a reversible data hiding (RDH) method in compressible encrypted images called the encryption-then-compression (EtC) images. The proposed method allows us to not only embed a payload in encrypted images but also compress the encrypted images containing the payload. In addition, the proposed RDH method can be applied to both plain images and encrypted ones, and the payload can be extracted flexibly in the encrypted domain or from the decrypted images. Various RDH methods have been studied in the encrypted domain, but they are not considered to be two-domain data hiding, and the resultant images cannot be compressed by using image coding standards, such as JPEG-LS and JPEG 2000. In our experiment, the proposed method shows high performance in terms of lossless compression efficiency by using JPEG-LS and JPEG 2000, data hiding capacity, and marked image quality.

In this paper, we propose L0 norm optimization in a scrambled sparse representation domain and its application to an Encryption-then-Compression (EtC) system. We design a random unitary transform that conserves L0 norm isometry. The resulting encryption method provides a practical orthogonal matching pursuit (OMP) algorithm that allows computation in the encrypted domain. We prove that the proposed method theoretically has exactly the same estimation performance as the nonencrypted variant of the OMP algorithm. In addition, we demonstrate the security strength of the proposed secure sparse representation when applied to the EtC system. Even if the dictionary information is leaked, the proposed scheme protects the privacy information of observed signals.

In this paper, we improve upon the automatic building extraction method, which uses a variational inference Gaussian mixture model for performing color clustering, by accelerating its computational speed. The improved method decreases the computational time using an image with reduced resolution upon applying color clustering. According to our experiment, in which we used 106 scenery images, the improved method could extract buildings at a rate 86.54% faster than that of the conventional methods. Furthermore, the improved method significantly increased the extraction accuracy by 1.8% or more by preventing over-clustering using the reduced image, which also had a reduced number of the colors.

A simple depth-key-based image composition is proposed, which uses two still images with depth information, background and foreground object. The proposed method can place the object at various locations in the background considering the depth in the 3D world coordinate system. The main feature is that a simple algorithm is provided, which enables us to achieve the depthward movement within the camera plane, without being aware of the 3D world coordinate system. Two algorithms are proposed (P-OMDD and O-OMDD), which are based on the pin-hole camera model. As an advantage, camera calibration is not required before applying the algorithm in these methods. Since a single image is used for the object representation, each of the proposed methods has its limitations in terms of fidelity of the composite image. P-OMDD faithfully reproduces the angle at which the object is seen, but the pixels of the hidden surface are missing. On the contrary, O-OMDD can avoid the hidden surface problem, but the angle of the object is fixed, wherever it moves. It is verified through several experiments that, when using O-OMDD, subjectively natural composite images can be obtained under any object movement, in terms of size and position in the camera plane. Future tasks include improving the change in illumination due to positional changes and the partial loss of objects due to noise in depth images.

A multi-task convolutional neural network leading to high performance and interpretability via attribute estimation is presented in this letter. Our method can provide interpretation of the classification results of CNNs by outputting attributes that explain elements of objects as a judgement reason of CNNs in the middle layer. Furthermore, the proposed network uses the estimated attributes for the following prediction of classes. Consequently, construction of a novel multi-task CNN with improvements in both of the interpretability and classification performance is realized.

In this letter, we propose a color quantization method based on saliency. In the proposed method, the salient colors are selected as representative colors preferentially by using saliency as weights. Through experiments, we verify the effectiveness of the proposed method.

In this letter, a quantitative evaluation index of contrast improvement of color images for dichromats is proposed. The index is made by adding two parameters to an existing index to make evaluation results consistent with human evaluation results. The effectiveness and validity of the proposed index are verified by experiments.

This article proposes a dynamic linkage service which is a specific service model of integrated broadcast — broadband services based ATSC 3.0. The dynamic linkage service is useful to the viewer who wants to continue watching programs using TV or their personal devices, even after the terrestrial broadcast ends due to the start of the next regular programming. In addition, we verify the feasibility of the proposed extended dynamic linkage service through developed emulation system based on ATSC 3.0. In consideration of the personal network capabilities of the viewer environment, the service was tested with 4K/2K Ultra HD and receiving the service was finished within 4 second over intranet.

Gaussian integer has a potential to enhance the safety of elliptic curve cryptography (ECC) on system under the condition fixing bit length of integral and floating point types, in viewpoint of the order of a finite field. However, there seems to have been no algorithm which makes Gaussian integer ECC safer under the condition. We present the algorithm to enhance the safety of ECC under the condition. Then, we confirm our Gaussian integer ECC is safer in viewpoint of the order of finite field than rational integer ECC or Gaussian integer ECC of naive methods under the condition.

Tweakable block cipher (TBC) is an extension of conventional block cipher. We study how to build a TBC based on generalized Feistel structure (GFS), a classical block cipher construction. While known dedicated TBC proposals are based on substitution-permutation network (SPN), GFS has not been used for building TBC. In particular, we take 64-bit GFS block cipher TWINE and try to make it tweakable with a minimum change. To find a best one from a large number of candidates, we performed a comprehensive search with a help of mixed integer linear programming (MILP) solver. As a result, our proposal TWINE is quite efficient, has the same number of rounds as TWINE with extremely simple tweak schedule.

Aggregate signature (AS) schemes enable anyone to compress signatures under different keys into one. In sequential aggregate signature (SAS) schemes, the aggregate signature is computed incrementally by the sighers. Several trapdoor-permutation-based SAS have been proposed. In this paper, we give a constructions of SAS based on the first SAS scheme with lazy verification proposed by Brogle et al. in ASIACRYPT 2012. In Brogle et al.'s scheme, the size of the aggregate signature is linear of the number of the signers. In our scheme, the aggregate signature has constant length which satisfies the original ideal of compressing the size of signatures.

This paper proposes a novel compressed sensing (CS) framework for reconstructing electroencephalogram (EEG) signals. A feature of this framework is the application of independent component analysis (ICA) to remove the interference from artifacts after undersampling in a data processing unit. Therefore, we can remove the ICA processing block from the sensing unit. In this framework, we used a random undersampling measurement matrix to suppress the Gaussian. The developed framework, in which the discrete cosine transform basis and orthogonal matching pursuit were used, was evaluated using raw EEG signals with a pseudo-model of an eye-blink artifact. The normalized mean square error (NMSE) and correlation coefficient (CC), obtained as the average of 2,000 results, were compared to quantitatively demonstrate the effectiveness of the proposed framework. The evaluation results of the NMSE and CC showed that the proposed framework could remove the interference from the artifacts under a high compression ratio.

A robust adaptive beamforming algorithm is proposed based on the precise interference-plus-noise covariance matrix reconstruction and steering vector estimation of the desired signal, even existing large gain-phase errors. Firstly, the model of array mismatches is proposed with the first-order Taylor series expansion. Then, an iterative method is designed to jointly estimate calibration coefficients and steering vectors of the desired signal and interferences. Next, the powers of interferences and noise are estimated by solving a quadratic optimization question with the derived closed-form solution. At last, the actual interference-plus-noise covariance matrix can be reconstructed as a weighted sum of the steering vectors and the corresponding powers. Simulation results demonstrate the effectiveness and advancement of the proposed method.

The notion of the signal-to-noise ratio (SNR), proposed by Guilley, et al. in 2004, is a property that attempts to characterize the resilience of (n,m)-functions F=(f₁,...,f_m) (cryptographic S-boxes) against differential power analysis. But how to study the signal-to-noise ratio for a Boolean function still appears to be an important direction. In this paper, we give a tight upper and tight lower bounds on SNR for any (balanced) Boolean function. We also deduce some tight upper bounds on SNR for balanced Boolean function satisfying propagation criterion. Moreover, we obtain a SNR relationship between an n-variable Boolean function and two (n-1)-variable decomposition functions. Meanwhile, we give SNR(f⊞g) and SNR(f⊡g) for any balanced Boolean functions f,g. Finally, we give a lower bound on SNR(F), which determined by SNR(f_i) (1≤i≤m), for (n,m)-function F=(f₁,f₂,…,f_m).

Expectation propagation (EP) decoding is proposed for sparse superposition coding in orthogonal frequency division multiplexing (OFDM) systems. When a randomized discrete Fourier transform (DFT) dictionary matrix is used, the EP decoding has the same complexity as approximate message-passing (AMP) decoding, which is a low-complexity and powerful decoding algorithm for the additive white Gaussian noise (AWGN) channel. Numerical simulations show that the EP decoding achieves comparable performance to AMP decoding for the AWGN channel. For OFDM systems, on the other hand, the EP decoding is much superior to the AMP decoding while the AMP decoding has an error-floor in high signal-to-noise ratio regime.

We propose an optimal power allocation scheme that maximizes the transmission rate of device-to-device (D2D) communications underlaying a cellular system based on orthogonal frequency division multiplexing (OFDM). The proposed algorithm first calculates the maximum allowed transmission power of a D2D transmitter to restrict the interference caused to a cellular link that share the same OFDM subchannels with the D2D link. Then, with a constraint on the maximum transmit power, an optimization of water-filling type is performed to find the optimal transmit power allocation across subchannels and within each subchannel. The performance of the proposed power allocation scheme is evaluated in terms of the average achievable rate of the D2D link.

In this work, we introduce notions of quantum frequency arrangements consisting of quantum frequency squares, cubes, hypercubes and a notion of orthogonality between them. We also propose a notion of quantum mixed orthogonal array (QMOA). By using irredundant mixed orthogonal array proposed by Goyeneche et al. we can obtain k-uniform states of heterogeneous systems from quantum frequency arrangements and QMOAs. Furthermore, some examples are presented to illustrate our method.