A fusion framework between CNN and RNN is proposed dedicatedly for air-writing recognition. By modeling the air-writing using both spatial and temporal features, the proposed network can learn more information than existing techniques. Performance of the proposed network is evaluated by using the alphabet and numeric datasets in the public database namely the 6DMG. Average accuracy of the proposed fusion network outperforms other techniques, i.e. 99.25% and 99.83% are observed in the alphabet gesture and the numeric gesture, respectively. Simplified structure of RNN is also proposed, which can attain about two folds speed-up of ordinary BLSTM network. It is also confirmed that only the distance between consecutive sampling points is enough to attain high recognition performance.

The aim of this work is to develop a method for the simultaneous analysis of multiple groups and their members based on hierarchical tensor manifold modeling. The method is particularly designed to analyze multiple teams, such as sports teams and business teams. The proposed method represents members' data using a nonlinear manifold for each team, and then these manifolds are further modeled using another nonlinear manifold in the model space. For this purpose, the method estimates the role of each member in the team, and discovers correspondences between members that play similar roles in different teams. The proposed method was applied to basketball league data, and it demonstrated the ability of knowledge discovery from players' statistics. We also demonstrated that the method could be used as a general tool for multi-level multi-group analysis by applying it to marketing data.

Automatic game strategy data acquisition is important for the realization of the professional strategy analysis systems by providing evaluation values such as the team status and the efficacy of plays. The key factor that influences the performance of the strategy data acquisition in volleyball game is the unknown player roles. Player role means the position with game meaning of each player in the team formation, such as the setter, attacker and blocker. The unknown player role makes individual player unreliable and loses the contribution of each player in the strategy analysis. This paper proposes a court-divisional team motion feature and a player performance curve to deal with the unknown player roles in strategy data acquisition. Firstly, the court-divisional team motion feature is proposed for the team tactical status detection. This feature reduces the influence of individual player information by summing up the ball relative motion density of all the players in divided court area, which corresponds to the different plays. Secondly, the player performance curves are proposed for the efficacy variables acquisition in attack play. The player roles candidates are detected by three features that represent the entire process of a player starting to rush (or jump) to the ball and hit the ball: the ball relative distance, ball approach motion and the attack motion feature. With the 3D ball trajectories and multiple players' positions tracked from multi-view volleyball game videos, the experimental detection rate of each team status (attack, defense-ready, offense-ready and offense status) are 75.2%, 84.2%, 79.7% and 81.6%. And for the attack efficacy variables acquisition, the average precision of the set zone, the number of available attackers, the attack tempo and the number of blockers are 100%, 100%, 97.8%, and 100%, which achieve 8.3% average improvement compared with manual acquisition.

A significant portion of computational resources of embedded systems for visual detection is dedicated to feature extraction, and this severely affects the detection accuracy and processing performance of the system. To solve this problem, we propose a feature descriptor based on histograms of oriented gradients (HOG) consisting of simple linear algebra that can extract equivalent information to the conventional HOG feature descriptor at a low computational cost. In an evaluation, a leading-edge detection algorithm with this decomposed vector HOG (DV-HOG) achieved equivalent or better detection accuracy compared with conventional HOG feature descriptors. A hardware implementation of DV-HOG occupies approximately 14.2 times smaller cell area than that of a conventional HOG implementation.

Binary sparse representation based on arbitrary quality metrics and its applications are presented in this paper. The novelties of the proposed method are twofold. First, the proposed method newly derives sparse representation for which representation coefficients are binary values, and this enables selection of arbitrary image quality metrics. This new sparse representation can generate quality metric-independent subspaces with simplification of the calculation procedures. Second, visual saliency is used in the proposed method for pooling the quality values obtained for all of the parts within target images. This approach enables visually pleasant approximation of the target images more successfully. By introducing the above two novel approaches, successful image approximation considering human perception becomes feasible. Since the proposed method can provide lower-dimensional subspaces that are obtained by better image quality metrics, realization of several image reconstruction tasks can be expected. Experimental results showed high performance of the proposed method in terms of two image reconstruction tasks, image inpainting and super-resolution.

Accurate visual correspondence is the foundation of many computer vision based applications. Since existing image matching algorithms generate mismatches inevitably, a reliable mismatch-removal algorithm is highly desired to remove mismatches and preserve true matches. This paper proposes a hierarchical progressive trust (HPT) model to solve this problem. The HPT model first adopts a “trust the most trustworthy ones” strategy to select anchor inliers in its bottom layer, and then progressively propagates the trust from bottom layer to other layers in a bottom-up way: 1) bottom layer verifies anchor inliers with the guidance of local features; 2) middle layers progressively estimate local transformations and perform local verifications; 3) top layer estimates a global transformation with an anchor-inliers-guided expectation maximization (EM) algorithm and performs global verifications. Experimental results show that the proposed HPT model achieves higher performance than state-of-the-art mismatch-removal methods under both rigid transformations and non-rigid deformations.

It is essential to improve intra prediction performance to raise the efficiency of video coding. In video coding standards such as H.265/HEVC, intra prediction is seen as an extension of directional prediction schemes, examples include refinement of directions, planar extension, filtering reference sampling, and so on. From the view point of reducing prediction error, some improvements on intra prediction for standardized schemes have been suggested. However, on the assumption that the correlation between neighboring pixels are static, these conventional methods use pre-defined predictors regardless of the image being encoded. Therefore, these conventional methods cannot reduce prediction error if the images break the assumption made in prediction design. On the other hand, adaptive predictors that change the image being encoded may offer poor coding efficiency due to the overhead of the additional information needed for adaptivity. This paper proposes an adaptive intra prediction scheme that resolves the trade-off between prediction error and adaptivity overhead. The proposed scheme is formulated as a constrained optimization problem that minimizes prediction error under sparsity constraints on the prediction coefficients. In order to solve this problem, a novel solver is introduced as an extension of LARS for multi-class support. Experiments show that the proposed scheme can reduce the amount of encoded bits by 1.21% to 3.24% on average compared to HM16.7.

This paper proposes a novel pseudo multi-exposure image fusion method based on a single image. Multi-exposure image fusion is used to produce images without saturation regions, by using photos with different exposures. However, it is difficult to take photos suited for the multi-exposure image fusion when we take a photo of dynamic scenes or record a video. In addition, the multi-exposure image fusion cannot be applied to existing images with a single exposure or videos. The proposed method enables us to produce pseudo multi-exposure images from a single image. To produce multi-exposure images, the proposed method utilizes the relationship between the exposure values and pixel values, which is obtained by assuming that a digital camera has a linear response function. Moreover, it is shown that the use of a local contrast enhancement method allows us to produce pseudo multi-exposure images with higher quality. Most of conventional multi-exposure image fusion methods are also applicable to the proposed multi-exposure images. Experimental results show the effectiveness of the proposed method by comparing the proposed one with conventional ones.

We propose the cube-based perceptual encryption (C-PE), which consists of cube scrambling, cube rotation, cube negative/positive transformation, and cube color component shuffling, and describe its application to the encryption-then-compression (ETC) system of Motion JPEG (MJPEG). Especially, cube rotation replaces the blocks in the original frames with ones in not only the other frames but also the depth-wise cube sides (spatiotemporal sides) unlike conventional block-based perceptual encryption (B-PE). Since it makes intra-block observation more difficult and prevents unauthorized decryption from only a single frame, it is more robust than B-PE against attack methods without any decryption key. However, because the encrypted frames including the blocks from the spatiotemporal sides affect the MJPEG compression performance slightly, we also devise a version of C-PE with no spatiotemporal sides (NSS-C-PE) that hardly affects compression performance. C-PE makes the encrypted video sequence robust against the only single frame-based algorithmic brute force (ABF) attack with only 21 cubes. The experimental results show the compression efficiency and encryption robustness of the C-PE/NSS-C-PE-based ETC system. C-PE-based ETC system shows mixed results depending on videos, whereas NSS-C-PE-based ETC system shows that the BD-PSNR can be suppressed to about -0.03dB not depending on videos.

An efficient two-layer coding method using the histogram packing technique with the backward compatibility to the legacy JPEG is proposed in this paper. The JPEG XT, which is the international standard to compress HDR images, adopts two-layer coding scheme for backward compatibility to the legacy JPEG. However, this two-layer coding structure does not give better lossless performance than the other existing methods for HDR image compression with single-layer structure. Moreover, the lossless compression of the JPEG XT has a problem on determination of the coding parameters; The lossless performance is affected by the input images and/or the parameter values. That is, finding appropriate combination of the values is necessary to achieve good lossless performance. It is firstly pointed out that the histogram packing technique considering the histogram sparseness of HDR images is able to improve the performance of lossless compression. Then, a novel two-layer coding with the histogram packing technique and an additional lossless encoder is proposed. The experimental results demonstrate that not only the proposed method has a better lossless compression performance than that of the JPEG XT, but also there is no need to determine image-dependent parameter values for good compression performance without losing the backward compatibility to the well known legacy JPEG standard.

This paper proposes gain relaxation in signal enhancement designed for speech recognition. Gain relaxation selectively applies softer enhancement of a target signal to eliminate potential degradation in speech recognition caused by small undesirable distortion in the target signal components. The softer enhancement is a solution to overlooked performance degradation in signal enhancement combined with speech recognition which is encountered in commercial products with an unaware small local noise source. Evaluation of directional interference suppression with signals recorded by a commercial PC (personal computer) demonstrates that signal enhancement over the input is achieved without sacrificing the performance for clean speech.

In this paper, we propose a secure near-field communication (NFC) for smartphones by combining acoustic and vibrational communication. In our hybrid system, a transmitter transmits an encrypted message and encryption key from a loudspeaker and vibration motor, respectively. While the sound emitted from the loudspeaker propagates through the air, the vibration emitted by the vibration motor propagates through the body of smartphones. Hence, only receivers touching the transmitter can receive both the encrypted message and the key, resulting in secure communication. We designed a software modulator and demodulator suitable for the vibrational communication by using return-to-zero (RZ) code. Then we established a hybrid communication system by combining acoustic and vibrational communication modems, and evaluated its performance in experiments. The results indicate that our hybrid system achieved a secure (among physically contacted devices) and fast (800kbps) NFC for smartphones.

A new projection-based color-to-gray conversion method is proposed in this letter. In the proposed method, an objective function which considers color contrasts in an input image is defined. Projection coefficients are determined by minimizing the objective function. Experimental results show the validity of the proposed method.

Adaptive noise cancellation using adaptive filters is a known method for removing noise that interferes with signal measurements. The adaptive noise canceller performs filtering based on the current situation through a windowing process. The shape of the window function determines the tracking performance of the adaptive noise canceller with respect to the fluctuation of the property of the unknown system that noise (reference signal) passes. However, the shape of the window function in the field of adaptive filtering has not yet been considered in detail. This study mathematically treats the effect of the window function on the adaptive noise canceller and proposes an optimization method for the window function in situations where offline processing can be performed, such as biomedical signal measurements. We also demonstrate the validity of the optimized window function through numerical experiments.

Optimal loads and maximum achievable efficiency for multiple-receiver inductive power transfer (IPT) system have been formulated by theoretical studies in literatures. This paper presents extended analysis on system behavior at optimal load condition and extensive S-parameter evaluation to validate the formulas. Our results confirm that at the optimal load condition, the system is in a resonance state; the impact of cross-coupling among receivers is completely mitigated; and the efficiency reaches its maximum expressed by an efficiency angle tangent, in an manner analogous to the well-known kQ-theory for single-receiver IPT. Our contributions do not lie in practical applications of multiple-receiver IPT but in establishing principles for design and benchmarking the system.

In this paper we present proofs for the new biases in RC4 which were experimentally found and listed out (without theoretical justifications and proofs) in a paper by Vanhoef et al. in USENIX 2015. Their purpose was to exploit the vulnerabilities of RC4 in TLS using the set of new biases found by them. We also show (and prove) new results on couple of very strong biases residing in the joint distribution of three consecutive output bytes of the RC4 stream cipher. These biases provides completely new distinguisher for RC4 taking roughly O(2²⁴) samples to distinguish streams of RC4 from a uniformly random stream. We also provide a list of new results with proofs relating to some conditional biases in the keystreams of the RC4 stream cipher.

Discrete Gaussian is a cornerstone of many lattice-based cryptographic constructions. Aiming at the orthogonal lattice of a vector, we propose a discrete Gaussian rejection sampling algorithm, by modifying the dynamic programming process for subset sum problems. Within O(nq²) time, our algorithm generates a distribution statistically indistinguishable from discrete Gaussian at width s>ω(log n). Moreover, we apply our sampling algorithm to general high-dimensional dense lattices, and orthogonal lattices of matrices $matAinZ_q^{O(1) imes n}$. Compared with previous polynomial-time discrete Gaussian samplers, our algorithm does not rely on the short basis.

As real-time embedded systems are required to accommodate various tasks with different levels of criticality, scheduling algorithms for MC (Mixed-Criticality) systems have been widely studied in the real-time systems community. Most studies have focused on MC uniprocessor systems whereas there have been only a few studies to support MC multiprocessor systems. In particular, although the ZL (Zero-Laxity) policy has been known to an effective technique in improving the schedulability performance of base scheduling algorithms on SC (Single-Criticality) multiprocessor systems, the effectiveness of the ZL policy on MC multiprocessor systems has not been revealed to date. In this paper, we focus on realizing the potential of the ZL policy for MC multiprocessor systems, which is the first attempt. To this end, we design the ZL policy for MC multiprocessor systems, and apply the policy to EDF (Earliest Deadline First), yielding EDZL (Earliest Deadline first until Zero-Laxity) tailored for MC multiprocessor systems. Then, we develop a schedulability analysis for EDZL (as well as its base algorithm EDF) to support its timing guarantee. Our simulation results show a significant schedulability improvement of EDZL over EDF, demonstrating the effectiveness of the ZL policy for MC multiprocessor systems.

Stochastic resonance (SR) is a phenomenon in which signal response in a nonlinear system is enhanced by noise. Fluctuating activities in deterministic chaos are known to cause a phenomenon called chaotic resonance (CR), which is similar to SR. Most previous studies on CR showed that these signal responses were controlled by internal parameters. However, in several applications of CR, it is difficult to control these parameters externally, particularly in biological systems. In this study, to overcome this difficulty, we propose a method for controlling the signal response of CR by adjusting the strength of external feedback control. By using this method, we demonstrate the control of CR in a one-dimensional cubic map, where CR arises from chaos-chaos switching to a weak input signal.

High-speed clock distribution design is becoming increasingly difficult and challenging task due to the huge power consumption and jitter caused by large capacitive loading and multiple repeater stages. This paper proposes a novel low-power, GHz-band bufferless LC-DCO which directly drives 10 mm on-chip clock distribution line for high-speed serial links. The shared LC-tank structure between DCO frequency tuning capacitor and clock distribution line mitigate the frequency sensitivity and makes an energy-efficient, area-saving, high-speed operation possible. The test-chip is implemented under TSMC 0.18µm, 1-poly, 6-metal CMOS technology and the core area of proposed LC-DCO is only 270×280µm². The full-chip post layout simulation results show 2.54GHz oscillation frequency, 2.2mA current consumption and phase noise of -123dBc/Hz at 1MHz offset.

A b-symbol read channel is a channel model in which b consecutive symbols are read at once. As special cases, it includes a symbol-pair read channel (b=2) and an ordinary channel (b=1). The sphere packing bound, the Gilbert-Varshamov (G-V) bound, and the asymptotic G-V bound for symbol-pair read channels are known for b=1 and 2. In this paper, we derive these three bounds for b-symbol read channels with b≥1. From analysis of the proposed G-V bound, it is confirmed that the achievable rate is higher for b-symbol read channels compared with those for ordinary channels based on the Hamming metric. Furthermore, it is shown that the optimal value of b that maximizes the asymptotic G-V bound is finitely determined depending on the fractional minimum distance.

In this paper, we consider coded multi-input multi-output (MIMO) systems with low-density parity-check (LDPC) codes and space-time block code (STBC) in MIMO channels. The LDPC code takes the role of a channel code while the STBC provides spatial-temporal diversity. The performance of such coded MIMO system has been shown to be excellent in the past. In this paper, we present a performance analysis for an upper bound on probability of error for coded MIMO schemes. Compared to previous works, the proposed approach for the upper bound can avoid any explicit weight enumeration of codewords and provide a significant step for the upper bound by using a multinomial theorem. In addition, we propose a log domain convolution that enables us to handle huge numbers, e.g., 10⁵⁰⁰. Comparison of system simulations and numerical evaluations shows that the proposed upper bound is applicable for various coded MIMO systems.

Nyquist folding receiver (NYFR) is a novel reconnaissance receiving architecture and it can realize wideband receiving with small amount of equipment. As a tradeoff of non-cooperative wideband receiving, the NYFR output will add an unknown key parameter that is called Nyquist zone (NZ) index. In this letter, we concentrate on the NZ index estimation of the NYFR output. Focusing on the basic pulse radar signals, the constant frequency signal, the binary phase coded signal and the linear frequency modulation signal are considered. The matching component function is proposed to estimate the NZ indexes of the NYFR outputs without the prior information of the signal modulation type. In addition, the relations between the matching component function and the parameters of the NYFR are discussed. Simulation results demonstrate the efficacy of the proposed method.

To improve detection performance for a reconnaissance receiver, which is designed to detect the non-cooperative MIMO-LFM radar signal under low SNR condition, this letter proposed a novel signal detection method. This method is based on Fractional Fourier Transform with entropy weight (FRFTE) and autocorrelation algorithm. In addition, the flow chart and feasibility of the proposed algorithm are analyzed. Finally, applying our method to Wigner Hough Transform (WHT), we demonstrate the superiority of this method by simulation results.

We studied complicated superstable periodic orbits (SSPOs) in a spiking neuron model with a rectangular threshold signal. The neuron exhibited SSPOs with various periods that changed dramatically when we varied the parameter space. Using a one-dimensional return map defined by the spike phase, we evaluated period changes and showed its complicated distribution. Finally, we constructed a test circuit to confirm the typical phenomena displayed by the mathematical model.

A summer-embedded sense amplifier (SE SA) is proposed to reduce feedback loop delay (T_FB) in a decision feedback equalizer (DFE). In the SE SA, the position of the ISI compensator is changed from the latch input to the latch output, and hence the T_FB is reduced. The simulated DFE achieves 32Gb/s and 66fJ/b with a 1-V 65-nm CMOS process.

Integer codes are defined by error-correcting codes over integers modulo a fixed positive integer. In this paper, we show that the construction of integer codes can be reduced into the cases of prime-power moduli. We can efficiently search integer codes with small prime-power moduli and can construct target integer codes with a large composite-number modulus. Moreover, we also show that this prime-factorization reduction is useful for the construction of self-orthogonal and self-dual integer codes, i.e., these properties in the prime-power moduli are preserved in the composite-number modulus. Numerical examples of integer codes and generator matrices demonstrate these facts and processes.

Compressed sensing theory provides a new approach to acquire data as a sampling technique and makes sure that a sparse signal can be reconstructed from few measurements. The construction of compressed sensing matrices is a main problem in compressed sensing theory (CS). In this paper, the deterministic constructions of compressed sensing matrices based on affine singular linear space over finite fields are presented and a comparison is made with the compressed sensing matrices constructed by DeVore based on polynomials over finite fields. By choosing appropriate parameters, our sparse compressed sensing matrices are superior to the DeVore's matrices. Then we use a new formulation of support recovery to recover the support sets of signals with sparsity no more than k on account of binary compressed sensing matrices satisfying disjunct and inclusive properties.

In this letter, two new families of quaternary sequences with low four-level or five-level autocorrelation are constructed based on generalized cyclotomy over Z_2p. These quaternary sequences are balanced and the maximal absolute value of the out-of-phase autocorrelation is 4.

This letter studies physical-layer security in a cognitive radio (CR) network, where a primary user (PU) is eavesdropped by multiple collusive eavesdroppers. Under the PU secrecy outage constraint to protect the PU, the secondary users (SUs) are assumed to be allowed to transmit. The problem of joint SU scheduling and power control to maximize the SU ergodic transmission rate is investigated for both the scenarios of perfect and imperfect channel state information (CSI). It is shown that, although collusive eavesdroppers degrade the PU performance compared to non-collusive eavesdroppers, the SU performance is actually improved when the number of eavesdroppers is large. It is also shown that our proposed scheme with imperfect CSI can guarantee that the PU performance is unaffected by imperfect CSI.

A simple and novel multiple-symbol differential detection (MSDD) scheme is proposed for IEEE 802.15.4 binary phase shift keying (BPSK) receivers. The detection is initiated by estimating and compensating the carrier frequency offset (CFO) effect in the chip sample of interest. With these new statistics, the decisions are jointly made by allowing the observation window length to be longer than two bit intervals. Simulation results demonstrate that detection reliability of the IEEE 802.15.4 BPSK receivers is significantly improved. Namely, at packet error rate (PER) of 1×10^-3, the signal-to-noise ratio (SNR) gap between ideal coherent detection (perfect carrier reference phase and no CFO) with differential decoding and conventional optimal single differential coherent detection (SDCD) is filled by 2.1dB when the observation window length is set to 6bit intervals. Then, the benefit that less energy consumed by retransmissions is successfully achieved.

In this letter, we present a class of binary sequences with optimal autocorrelation magnitude. Compared with Krengel-Ivanov sequences, some proposed sequences have different autocorrelation distribution. This indicates those sequences would be new. As an application of constructed binary sequences, we derive a class of quaternary sequences of length 4p with autocorrelation magnitude equal to $2sqrt{2}$, which is lower than the autocorrelation magnitude equal to 4 of Chung-Han-Yang sequences given in 2011.

This letter proposes a novel dynamic channel assignment (DCA) scheme with consideration of interference and fairness for the downlink of dense small-cell networks based on orthogonal frequency division multiple access-frequency division duplex. In the proposed scheme, a small-cell gateway fairly assigns subchannels to the small-cell user equipment (SUE) according to the co-tier interference from neighboring small-cell access points. From the simulation results, it is shown that the proposed DCA scheme outperforms other DCA schemes in terms of the fairness of each SUE capacity.

In this letter, we consider the harvested-energy fairness problem in cognitive multicast systems with simultaneous wireless information and power transfer. In the cognitive multicast system, a cognitive transmitter with multi-antenna sends the same information to cognitive users in the presence of licensed users, and cognitive users can decode information and harvest energy with a power-splitting structure. The harvested-energy fairness problem is formulated and solved by using two proposed algorithms, which are based on semidefinite relaxation with majorization-minimization method, and sequential parametric convex approximation with feasible point pursuit technique, respectively. Finally, the performances of the proposed solutions and baseline schemes are verified by simulation results.

Intensity modulated radiation therapy (IMRT), which irradiates doses to a target organ, calculates the irradiation dose using the radiation treatment planning system (RTPS). The irradiation quality is ensured by verifying that the dose distribution planned by RTPS is the same as the data measured by two-dimensional (2D) detectors. Since an actual three-dimensional (3D) distribution of irradiated dose spreads complicatedly, it is different from that of RTPS. Therefore, it is preferable to evaluate by using not only RTPS, but also actual irradiation dose distribution. In this paper, in order to perform a dose-volume histogram (DVH) evaluation of the irradiation dose distribution, we propose a method of correcting the dose distribution of RTPS by using sparsely measured radial data from 2D dose detectors. And we perform a DVH evaluation of irradiation dose distribution and we show that the proposed method contributes to high-precision DVH evaluation. The experimental results show that the estimates are in good agreement with the measured data from the 2D detectors and that the peak signal to noise ratio and the structural similarity indexes of the estimates are more accurate than those of RTPS. Therefore, we present the possibility of an evaluation of the actual irradiation dose distribution using measured data in a limited observation direction.

Hazes with various properties spread widely across flat areas with depth continuities and corner areas with depth discontinuities. Removing haze from a single hazy image is difficult due to its ill-posed nature. To solve this problem, this study proposes a modified hybrid median filter that performs a median filter to preserve the edges of flat areas and a hybrid median filter to preserve depth discontinuity corners. Recovered scene radiance, which is obtained by removing hazy particles, restores image visibility using adaptive nonlinear curves for dynamic range expansion. Using comparative studies and quantitative evaluations, this study shows that the proposed method achieves similar or better results than those of other state-of-the-art methods.

In this letter, we propose a supervised bimodal emotion recognition approach based on two important human emotion modalities including facial expression and body gesture. A effectively supervised feature fusion algorithms named supervised multiset canonical correlation analysis (SMCCA) is presented to established the linear connection between three sets of matrices, which contain the feature matrix of two modalities and their concurrent category matrix. The test results in the bimodal emotion recognition of the FABO database show that the SMCCA algorithm can get better or considerable efficiency than unsupervised feature fusion algorithm covering canonical correlation analysis (CCA), sparse canonical correlation analysis (SCCA), multiset canonical correlation analysis (MCCA) and so on.