In the theory of orthogonal arrays, an orthogonal array (OA) is called schematic if its rows form an association scheme with respect to Hamming distances. In this paper, we study the Hamming distances of any two rows in an OA, construct some schematic OAs of strength two and give the positive solution to the open problem for classifying all schematic OAs. Some examples are given to illustrate our methods.

As one of the most popular intelligent optimization algorithms, Simulated Annealing (SA) faces two key problems, the generation of perturbation solutions and the control strategy of the outer loop (cooling schedule). In this paper, we introduce the Gaussian Cloud model to solve both problems and propose a novel cloud annealing algorithm. Its basic idea is to use the Gaussian Cloud model with decreasing numerical character He (Hyper-entropy) to generate new solutions in the inner loop, while He essentially indicates a heuristic control strategy to combine global random search of the outer loop and local tuning search of the inner loop. Experimental results in function optimization problems (i.e. single-peak, multi-peak and high dimensional functions) show that, compared with the simple SA algorithm, the proposed cloud annealing algorithm will lead to significant improvement on convergence and the average value of obtained solutions is usually closer to the optimal solution.

Group signatures are signatures providing signer anonymity where signers can produce signatures on behalf of the group that they belong to. Although such anonymity is quite attractive considering privacy issues, it is not trivial to check whether a signer has been revoked or not. Thus, how to revoke the rights of signers is one of the major topics in the research on group signatures. In particular, scalability, where the signing and verification costs and the signature size are constant in terms of the number of signers N, and other costs regarding signers are at most logarithmic in N, is quite important. In this paper, we propose a revocable group signature scheme which is currently more efficient compared to previous all scalable schemes. Moreover, our revocable group signature scheme is secure under simple assumptions (in the random oracle model), whereas all scalable schemes are secure under q-type assumptions. We implemented our scheme by employing a Barreto-Lynn-Scott curve of embedding degree 12 over a 455-bit prime field (BLS-12-455), and a Barreto-Naehrig curve of embedding degree 12 over a 382-bit prime field (BN-12-382), respectively, by using the RELIC library. We showed that the online running times of our signing algorithm were approximately 14msec (BLS-12-455) and 11msec (BN-12-382), and those of our verification algorithm were approximately 20msec (BLS-12-455) and 16msec (BN-12-382), respectively. Finally, we showed that our scheme (with a slight extension) is applied to an identity management system proposed by Isshiki et al.

A light field, which is equivalent to a dense set of multi-view images, has various applications such as depth estimation and 3D display. One of the essential problems in light field applications is light field interpolation, i.e., view interpolation. The interpolation accuracy is enhanced by exploiting an inherent property of a light field. One example is that an epipolar plane image (EPI), which is a 2D subset of the 4D light field, consists of many lines, and these lines have almost the same slope in a local region. This structure induces a sparse representation in the frequency domain, where most of the energy resides on a line passing through the origin. On the basis of this observation, we propose a group sparsity prior suitable for light fields to exploit their line structure fully for interpolation. Specifically, we designed the directional groups in the discrete Fourier transform (DFT) domain so that the groups can represent the concentration of the energy, and we thereby formulated an LF interpolation problem as an overlapping group lasso. We also introduce several techniques to improve the interpolation accuracy such as applying a window function, determining group weights, expanding processing blocks, and merging blocks. Our experimental results show that the proposed method can achieve better or comparable quality as compared to state-of-the-art LF interpolation methods such as convolutional neural network (CNN)-based methods.

(k,n)-visual secret sharing scheme ((k,n)-VSSS) is a method to divide a secret image into n images called shares that enable us to restore the original image by only stacking at least k of them without any complicated computations. In this paper, we consider (2,2)-VSSS to share two secret images at the same time only by two shares, and investigate the methods to improve the quality of decoded images. More precisely, we consider (2,2)-VSSS in which the first secret image is decoded by stacking those two shares in the usual way, while the second one is done by stacking those two shares in the way that one of them is used reversibly. Since the shares must have some subpixels that inconsistently correspond to pixels of the secret images, the decoded pixels do not agree with the corresponding pixels of the secret images, which causes serious degradation of the quality of decoded images. To reduce such degradation, we propose several methods to construct shares that utilize 8-neighbor Laplacian filter and halftoning. Then we show that the proposed methods can effectively improve the quality of decoded images. Moreover, we demonstrate that the proposed methods can be naturally extended to (2,2)-VSSS for RGB images.

To enhance cover song identification accuracy on a large-size music archive, a song-level feature summarization method is proposed by using multi-scale representation. The chroma n-grams are extracted in multiple scales to cope with both global and local tempo changes. We derive index from the extracted n-grams by clustering to reduce storage and computation for DB search. Experiments on the widely used music datasets confirmed that the proposed method achieves the state-of-the-art accuracy while reducing cost for cover song search.

This letter presents an improved hybrid direction of arrival (DOA) estimation scheme with computational efficiency for massive uniform linear array. In order to enhance the resolution of DOA estimation, the initial estimator based on the discrete Fourier transform is applied to obtain coarse DOA estimates by a virtual array extension for one snapshot. Then, by means of a first-order Taylor series approximation to the direction vector with the one initially estimated in a very small region, the iterative fine estimator can find a new direction vector which raises the searching efficiency. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

In wireless multi-hop networks such as ad hoc networks and sensor networks, backoff-based opportunistic routing protocols, which make a forwarding decision based on backoff time, have been proposed. In the protocols, each potential forwarder calculates the backoff time based on the product of a weight and global scaling factor. The weight prioritizes potential forwarders and is calculated based on hop counts to the destination of a sender and receiver. The global scaling factor is a predetermined value to map the weight to the actual backoff time. However, there are three common issues derived from the global scaling factor. First, it is necessary to share the predetermined global scaling factor with a centralized manner among all terminals properly for the backoff time calculation. Second, it is almost impossible to change the global scaling factor during the networks are being used. Third, it is difficult to set the global scaling factor to an appropriate value since the value differs among each local surrounding of forwarders. To address the aforementioned issues, this paper proposes a novel decentralized local scaling factor control without relying on a predetermined global scaling factor. The proposed method consists of the following three mechanisms: (1) sender-centric local scaling factor setting mechanism in a decentralized manner instead of the global scaling factor, (2) adaptive scaling factor control mechanism which adapts the local scaling factor to each local surrounding of forwarders, and (3) mitigation mechanism for excessive local scaling factor increases for the local scaling factor convergence. Finally, this paper evaluates the backoff-based opportunistic routing protocol with and without the proposed method using computer simulations.

The flexibility of wireless communication makes it more and more widely used in industrial scenarios. To satisfy the strict real-time requirements of industry, various wireless methods especially based on the time division multiple access protocol have been introduced. In this work, we first conduct a mathematical analysis of the network model and the problem of minimum packet loss. Then, an optimal Real-time Scheduling algorithm based on Backtracking method (RSBT) for industrial wireless sensor networks is proposed; this yields a scheduling scheme that can achieve the lowest network packet loss rate. We also propose a suboptimal Real-time Scheduling algorithm based on Urgency and Concurrency (RSUC). Simulation results show that the proposed algorithms effectively reduce the rate of the network packet loss and the average response time of data flows. The real-time performance of the RSUC algorithm is close to optimal, which confirms the computation efficiency of the algorithm.

We propose a novel compositing pipeline and a dynamic load balancing technique for volume rendering which utilizes a two-layered group structure to achieve effective and scalable load balancing. The technique enables each process to render data from non-contiguous regions of the volume with minimal impact on the total render time. We demonstrate the effectiveness of the proposed technique by performing a set of experiments on a modern GPU cluster. The experiments show that using the technique results in up to a 35.7% lower worst-case memory usage as compared to a dynamic k-d tree load balancing technique, whilst simultaneously achieving similar or higher render performance. The proposed technique was also able to lower the amount of transferred data during the load balancing stage by up to 72.2%. The technique has the potential to be used in many scenarios where other dynamic load balancing techniques have proved to be inadequate, such as during large-scale visualization.

Mobile video traffic is expected to increase explosively because of the proliferating number of Wi-Fi terminals. An overloaded multiple-input multiple-output (MIMO) technique allows the receiver to implement smaller number of antennas than the transmitter in exchange for degradation in video quality and a large amount of computational complexity for postcoding at the receiver side. This paper proposes a novel linear precoder for high-quality video streaming in overloaded multiuser MIMO systems, which protects visually significant portions of a video stream. A low complexity postcoder is also proposed, which detects some of data symbols by linear detection and the others by a prevoting vector cancellation (PVC) approach. It is shown from simulation results that the combination use of the proposed precoder and postcoder achieves higher-quality video streaming to multiple users in a wider range of signal-to-noise ratio (SNR) than a conventional unequal error protection scheme. The proposed precoder attains 40dB in peak signal-to-noise ratio even in poor channel conditions such as the SNR of 12dB. In addition, due to the stepwise acquisition of data symbols by means of linear detection and PVC, the proposed postcoder reduces the number of complex additions by 76% and that of multiplications by 64% compared to the conventional PVC.

Backscatter wireless communications offer advantages such as batteryless operations, small form factor, and radio regulatory exemption sensors. The major challenge ahead of backscatter wireless communications is synchronized multicarrier data collection, which can be realized by rejecting mutual harmonics among backscatters. This paper analyzes the mutual interferences of digitally modulated multicarrier backscatter to find interferences from higher frequency subcarriers to lower frequency subcarriers, which do not take place in analog modulated multicarrier backscatters, is harmful for densely populated subcarriers. This reverse interference distorts the harmonics replica, deteriorating the performance of the existing method, which rejects mutual interference among subcarriers by 5dB processing gain. To solve this problem, this paper analyzes the relationship between subcarrier spacing and reverse interference, and reveals that an alternate channel spacing, with channel separation twice the bandwidth of a subcarrier, can provide reasonably dense subcarrier allocation and can alleviate reverse interference. The idea is examined with prototype sensors in a wired experiment and in an indoor propagation experiment. The results reveal that with alternate channel spacing, the reverse interference practically becomes negligible, and the existing interference rejection method achieves the original processing gain of 5dB with one hundredth packet error rate reduction.

A one-dimensional lattice of tunnel-diode oscillators is investigated for potential high-speed frequency divider. In the evolution of the investigated lattice, the high-frequency oscillation dominates over the low-frequency oscillation. When a base oscillator is connected at the end, and generates oscillatory signals with a frequency higher than that of the synchronous lattice oscillation, the oscillator output begins to move in the lattice. This one-way property guarantees that the oscillation dynamics of the lattice have only slight influence on the oscillator motion. Moreover, counter-moving pulses in the lattice exhibit pair annihilation through head-on collisions. These lattice properties enable an efficient frequency division method. Herein, the operating principles of the frequency divider are described, along with a numerical validation.

The windowed interpolation DFT methods have been utilized to estimate the parameters of a single frequency and multi-frequency signal. Nevertheless, they do not work well for the real-valued sinusoids with closely spaced positive- and negative- frequency. In this paper, we describe a novel three-point windowed interpolation DFT method for frequency measurement of real-valued sinusoid signal. The exact representation of the windowed DFT with maximum sidelobe decay window (MSDW) is constructed. The spectral superposition of positive- and negative-frequency is considered and calculated to improve the estimation performance. The simulation results match with the theoretical values well. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.

MQTT is one of the promising protocols for various data exchange in IoT environments. Typically, those environments have a characteristic called “edge-heavy”, which means that things at the network edge generate a massive volume of data with high locality. For handling such edge-heavy data, an architecture of placing multiple MQTT brokers at the network edges and making them cooperate with each other is quite effective. It can provide higher throughput and lower latency, as well as reducing consumption of cloud resources. However, under this kind of architecture, heterogeneity could be a vital issue. Namely, an appropriate product of MQTT broker could vary according to the different environment of each network edge, even though different products are hard to cooperate due to the MQTT specification providing no interoperability between brokers. In this paper, we propose Interworking Layer of Distributed MQTT brokers (ILDM), which enables arbitrary kinds of MQTT brokers to cooperate with each other. ILDM, designed as a generic mechanism independent of any specific cooperation algorithm, provides APIs to facilitate development of a variety of algorithms. By using the APIs, we also present two basic cooperation algorithms. To evaluate the usefulness of ILDM, we introduce a benchmark system which can be used for both a single broker and multiple brokers. Experimental results show that the throughput of five brokers running together by ILDM is improved 4.3 times at maximum than that of a single broker.

Spoofing attacks are one of the biggest concerns for most biometric recognition systems. This will be also the case with silhouette-based gait recognition in the near future. So far, gait recognition has been fortunately out of the scope of spoofing attacks. However, it is becoming a real threat with the rapid growth and spread of deep neural network-based multimedia generation techniques, which will allow attackers to generate a fake video of gait silhouettes resembling a target person's walking motion. We refer to such computer-generated fake silhouettes as gait silhouette clones (GSCs). To deal with the future threat caused by GSCs, in this paper, we propose a supervised method for discriminating GSCs from genuine gait silhouettes (GGSs) that are observed from actual walking people. For training a good discriminator, it is important to collect training datasets of both GGSs and GSCs which do not differ from each other in any aspect other than genuineness. To this end, we propose to generate a training set of GSCs from GGSs by transforming them using multiple autoencoders. The generated GSCs are used together with their original GGSs for training the discriminator. In our experiments, the proposed method achieved the recognition accuracy of up to 94% for several test datasets, which demonstrates the effectiveness and the generality of the proposed method.

Super resolution (SR) image reconstruction has attracted increasing attention these years and many SR image reconstruction algorithms have been proposed for restoring a high-resolution image from one or multiple low-resolution images. However, how to objectively evaluate the quality of SR reconstructed images remains an open problem. Although a great number of image quality metrics have been proposed, they are quite limited to evaluate the quality of SR reconstructed images. Inspired by this, this paper presents a blind quality index for SR reconstructed images using first- and second-order structural degradation. First, the SR reconstructed image is decomposed into multi-order derivative magnitude maps, which are effective for first- and second-order structural representation. Then, log-energy based features are extracted on these multi-order derivative magnitude maps in the frequency domain. Finally, support vector regression is used to learn the quality model for SR reconstructed images. The results of extensive experiments that were conducted on one public database demonstrate the superior performance of the proposed method over the existing quality metrics. Moreover, the proposed method is less dependent on the number of training images and has low computational cost.

The estimation of the matrix rank of harmonic components of a music spectrogram provides some useful information, e.g., the determination of the number of basis vectors of the matrix-factorization-based algorithms, which is required for the automatic music transcription or in post-processing. In this work, we develop an algorithm based on Stein's unbiased risk estimator (SURE) algorithm with the matrix factorization model. The noise variance required for the SURE algorithm is estimated by suppressing the harmonic component via median filtering. An evaluation performed using the MIDI-aligned piano sounds (MAPS) database revealed an average estimation error of -0.26 (standard deviation: 4.4) for the proposed algorithm.

Multi-Mouse Quiz (MMQ) is a quiz application based on the Single Display Groupware (SDG)[1] concept through which several users can answer quizzes by sharing a computer to take the quiz in a classroom or any other learning environment. We conducted a practice, where we used the MMQ to support collaborative learning, which was combined with a museum visit. In the previous research, we found that the 3rd-grade children were able to operate the MMQ without any special assistance from the researchers, and that their use of the MMQ was characterized by high engagement[2]. In this study, we also conducted qualitative evaluation in the form of observation data and a free description of the questionnaire; we found that, compared to previous studies, which used MMQ with 6th-grade children, the 3rd-grade were more willing to use body language to express their emotions, and this tendency made the whole class more active. Furthermore, MMQ quiz learning inspired children with reflection perspectives to participate in the museum activity and activities in the computer room.

Most existing truth discovery approaches are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data for its unique characteristics such as multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning). As for text answers, there are no absolute correctness or errors, most answers may be partially correct, which is quite different from the situation of traditional truth discovery. To solve these challenges, we propose an optimization-based text truth discovery model which jointly groups keywords extracted from the answers of the specific question into a set of multiple factors. Then, we select the subset of multiple factors as identified truth set for each question by parallel ant colony synchronization optimization algorithm. After that, the answers to each question can be ranked based on the similarities between factors answer provided and identified truth factors. The experiment results on real dataset show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art approaches.