This paper considers a velocity control problem for merging and splitting maneuvers of vehicle platoons. In this paper, an external device sends velocity commands to some vehicles in the platoon, and the others adjust their velocities autonomously. The former is pinning control, and the latter is consensus control in multi-agent control. We propose a switched pinning control algorithm. Our algorithm consists of three sub-methods. The first is an optimal switching method of pinning agents based on an MLD (Mixed Logical Dynamical) system model and MPC (Model Predictive Control). The second is a representation method for dynamical platoon formation with merging and splitting maneuver. The platoon formation follows the positional relation between vehicles or the formation demand from the external device. The third is a switching reduction method by setting a cost function that penalizes the switching of the pinning agents in the steady-state. Our proposed algorithm enables us to improve the consensus speed. Moreover, our algorithm can regroup the platoons to the arbitrary platoons and control the velocities of the multiple vehicle platoons to each target value.

In order not to disrupt a team member concentrating on his/her own task, the interrupter needs to wait for a proper time. In this research, we examined the feasibility of predicting prospective interruptible times of office workers who use PCs. An analysis of actual working data collected from 13 participants revealed the relationship between uninterruptible durations and four features, i.e. type of application software, rate of PC operation activity, activity ratio between keystrokes and mouse clicks, and switching frequency of application software. On the basis of these results, we developed a probabilistic work continuance model whose probability changes according to the four features. The leave-one-out cross-validation indicated positive correlations between the actual and the predicted durations. The medians of the actual and the predicted durations were 539 s and 519 s. The main contribution of this study is the demonstration of the feasibility to predict uninterruptible durations in an actual working scenario.

Cross-component linear model (CCLM) has been recently adopted as a chroma intra-prediction tool in Versatile Video Coding (VVC), which is being developed as a new video coding standard. CCLM predicts chroma components from luma components through a linear model based on assumption of linear correlation between both components. A linear model is derived from the reconstructed neighboring luma and chroma samples of the current coding block by linear regression. A simplified linear modeling method recently adopted in the test model of VVC (VTM) 3.0 significantly reduces computational complexity of deriving model parameters with considerable coding loss. This letter proposes a method of linear modeling to compensate the coding loss of the simplified linear model. In the proposed method, the model parameters which are quite roughly derived in the existing simplified linear model are refined more accurately using individual method to derive each parameter. Experimental results show that, compared to VTM 3.0, the proposed method gives 0.08%, 0.52% and 0.55% Bjotegaard-Delta (BD)-rate savings, for Y, Cb and Cr components, respectively, in the All-Intra (AI) configuration with negligible computational complexity increase.

The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.

The emerging Versatile Video Coding (VVC) standard currently adopts Triangular Partitioning Mode (TPM) to make more flexible inter prediction. Due to the motion search and motion storage for TPM, the complexity of the encoder and decoder is significantly increased. This letter proposes two simplifications of TPM for reducing the complexity of the current design. One simplification is to reduce the number of combinations of motion vectors for both partitions to be checked. The method gives 4% encoding time decrease with negligible BD-rate loss. Another one is to remove the reference picture remapping process in the motion vector storage of TPM. It reduces the complexity of the encoder and decoder without a BD-rate change for the random-access configuration.

With the recent advances in e-commerce, it has become important to recommend not only mass-produced daily items, such as books, but also items that are not mass-produced. In this study, we present an algorithm for real estate recommendations. Automatic property recommendations are a highly difficult task because no identical properties exist in the world, occupied properties cannot be recommended, and users rent or buy properties only a few times in their lives. For the first step of property recommendation, we predict users' preferences for properties by combining content-based filtering and Multi-Layer Perceptron (MLP). In the MLP, we use not only attribute data of users and properties, but also deep features extracted from property floor plan images. As a result, we successfully predict users' preference with a Matthews Correlation Coefficient (MCC) of 0.166.

In the context of compressed sensing (CS), simultaneous orthogonal matching pursuit (SOMP) algorithm is an important iterative greedy algorithm for multiple measurement matrix vectors sharing the same non-zero locations. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the convergence of CS algorithms. Based on the RIP of measurement matrix, this paper shows that for the K-row sparse recovery, the restricted isometry constant (RIC) is improved to $delta_{K+1}<rac{sqrt{4K+1}-1}{2K}$ for SOMP algorithm. In addition, based on this RIC, this paper obtains sufficient conditions that ensure the convergence of SOMP algorithm in noisy case.

In this paper, we propose secure dictionary learning based on a random unitary transform for sparse representation. Currently, edge cloud computing is spreading to many application fields including services that use sparse coding. This situation raises many new privacy concerns. Edge cloud computing poses several serious issues for end users, such as unauthorized use and leak of data, and privacy failures. The proposed scheme provides practical MOD and K-SVD dictionary learning algorithms that allow computation on encrypted signals. We prove, theoretically, that the proposal has exactly the same dictionary learning estimation performance as the non-encrypted variant of MOD and K-SVD algorithms. We apply it to secure image modeling based on an image patch model. Finally, we demonstrate its performance on synthetic data and a secure image modeling application for natural images.

We propose a method of non-blind speech watermarking based on direct spread spectrum (DSS) using a linear prediction scheme to solve sound distortion due to spread spectrum. Results of evaluation simulations revealed that the proposed method had much lower sound-quality distortion than the DSS method while having almost the same bit error ratios (BERs) against various attacks as the DSS method.

In this letter, a construction of sparse measurement matrices is presented. Based on finite fields, a base matrix is obtained. Then a Hadamard matrix or a discrete Fourier transform (DFT) matrix is nested in the base matrix, which eventually formes a new deterministic measurement matrix. The coherence of the proposed matrices is low, which meets the Welch bound asymptotically. Thus these matrices could satisfy the restricted isometry property (RIP). Simulation results demonstrate that the proposed matrices give better performance than Gaussian counterparts.

We propose a method to realize a subjective super-resolution on a high-speed LED display, which dynamically shows a set of four neighboring pixels on every LED pixel. We have experimentally confirmed the subjective super-resolution effect. This paper proposes a subjective super-resolution hypothesis in human visual system and reports simulation results with pseudo fixation eye movements.

Although correlation filter-based trackers have demonstrated excellent performance for visual object tracking, there remain several challenges to be addressed. In this work, we propose a novel tracker based on the correlation filter framework. Traditional trackers face difficulty in accurately adapting to changes in the scale of the target when the target moves quickly. To address this, we suggest a scale adaptive scheme based on prediction scales. We also incorporate a speed-based adaptive model update method to further improve overall tracking performance. Experiments with samples from the OTB100 and KITTI datasets demonstrate that our method outperforms existing state-of-the-art tracking algorithms in fast motion scenes.

Fault prediction aims to identify whether a software module is defect-prone or not according to metrics that are mined from software projects. These metric values, also known as features, may involve irrelevance and redundancy, which hurt the performance of fault prediction models. In order to filter out irrelevant and redundant features, a Hybrid Feature Selection (abbreviated as HFS) method for software fault prediction is proposed. The proposed HFS method consists of two major stages. First, HFS groups features with hierarchical agglomerative clustering; second, HFS selects the most valuable features from each cluster to remove irrelevant and redundant ones based on two wrapper based strategies. The empirical evaluation was conducted on 11 widely-studied NASA projects, using three different classifiers with four performance metrics (precision, recall, F-measure, and AUC). Comparison with six filter-based feature selection methods demonstrates that HFS achieves higher average F-measure and AUC values. Compared with two classic wrapper feature selection methods, HFS can obtain a competitive prediction performance in terms of average AUC while significantly reducing the computation cost of the wrapper process.

Boolean functions used in the filter model of stream ciphers should have balancedness, large nonlinearity, optimal algebraic immunity and high algebraic degree. Besides, one more criterion called strict avalanche criterion (SAC) can be also considered. During the last fifteen years, much work has been done to construct balanced Boolean functions with optimal algebraic immunity. However, none of them has the SAC property. In this paper, we first present a construction of balanced Boolean functions with SAC property by a slight modification of a known method for constructing Boolean functions with SAC property and consider the cryptographic properties of the constructed functions. Then we propose an infinite class of balanced functions with optimal algebraic immunity and SAC property in odd number of variables. This is the first time that such kind of functions have been constructed. The algebraic degree and nonlinearity of the functions in this class are also determined.

Upcoming mood prediction plays an important role in different topics such as bipolar depression disorder in psychology and quality-of-life and recommendations on health-related quality of life research. The mood in this study is defined as the general emotional state of a user. In contrast to emotions which is more specific and varying within a day, the mood is described as having either a positive or negative valence[1]. We propose an autonomous system that predicts the upcoming user mood based on their online activities over cyber, social and physical spaces without using extra-devices and sensors. Recently, many researchers have relied on online social networks (OSNs) to detect user mood. However, all the existing works focused on inferring the current mood and only few works have focused on predicting the upcoming mood. For this reason, we define a new goal of predicting the upcoming mood. We, first, collected ground truth data during two months from 383 subjects. Then, we studied the correlation between extracted features and user's mood. Finally, we used these features to train two predictive systems: generalized and personalized. The results suggest a statistically significant correlation between tomorrow's mood and today's activities on OSNs, which can be used to develop a decent predictive system with an average accuracy of 70% and a recall of 75% for the correlated users. This performance was increased to an average accuracy of 79% and a recall of 80% for active users who have more than 30 days of history data. Moreover, we showed that, for non-active users, referring to a generalized system can be a solution to compensate the lack of data at the early stage of the system, but when enough data for each user is available, a personalized system is used to individually predict the upcoming mood.

With the acceptance of social sharing, public bike sharing services have become popular worldwide. One of the most important tasks in operating a bike sharing system is managing the bike supply at each station to avoid either running out of bicycles or docks to park them. This requires the system operator to redistribute bicycles from overcrowded stations to under-supplied ones. Trip demand prediction plays a crucial role in improving redistribution strategies. Predicting trip demand is a highly challenging problem because it is influenced by multiple levels of factors, both environmental and individual, e.g., weather and user characteristics. Although several existing studies successfully address either of them in isolation, no framework exists that can consider all factors simultaneously. This paper starts by analyzing trip data from real-world bike-sharing systems. The analysis reveals the interplay of the multiple levels of the factors. Based on the analysis results, we develop a novel form of the point process; it jointly incorporates multiple levels of factors to predict trip demand, i.e., predicting the pick-up and drop-off levels in the future and when over-demand is likely to occur. Our extensive experiments on real-world bike sharing systems demonstrate the superiority of our trip demand prediction method over five existing methods.

The sparse unmixing of hyperspectral data has attracted much attention in recent years because it does not need to estimate the number of endmembers nor consider the lack of pure pixels in a given hyperspectral scene. However, the high mutual coherence of spectral libraries strongly affects the practicality of sparse unmixing. The collaborative sparse unmixing via variable splitting and augmented Lagrangian (CLSUnSAL) algorithm is a classic sparse unmixing algorithm that performs better than other sparse unmixing methods. In this paper, we propose a CLSUnSAL-based hyperspectral unmixing method based on dictionary pruning and reweighted sparse regression. First, the algorithm identifies a subset of the original library elements using a dictionary pruning strategy. Second, we present a weighted sparse regression algorithm based on CLSUnSAL to further enhance the sparsity of endmember spectra in a given library. Third, we apply the weighted sparse regression algorithm on the pruned spectral library. The effectiveness of the proposed algorithm is demonstrated on both simulated and real hyperspectral datasets. For simulated data cubes (DC1, DC2 and DC3), the number of the pruned spectral library elements is reduced by at least 94% and the runtime of the proposed algorithm is less than 10% of that of CLSUnSAL. For simulated DC4 and DC5, the runtime of the proposed algorithm is less than 15% of that of CLSUnSAL. For the real hyperspectral datasets, the pruned spectral library successfully reduces the original dictionary size by 76% and the runtime of the proposed algorithm is 11.21% of that of CLSUnSAL. These experimental results show that our proposed algorithm not only substantially improves the accuracy of unmixing solutions but is also much faster than some other state-of-the-art sparse unmixing algorithms.

In this paper, we propose a Pyramid Predictive Attention Network (PPAN) for medical image segmentation. In the medical field, the size of dataset generally restricts the performance of deep CNN and deploying the trained network with gross parameters into the terminal device with limited memory is an expectation. Our team aims to the future home medical diagnosis and search for lightweight medical image segmentation network. Therefore, we designed PPAN mainly made of Xception blocks which are modified from DeepLab v3+ and consist of separable depthwise convolutions to speed up the computation and reduce the parameters. Meanwhile, by utilizing pyramid predictions from each dimension stage will guide the network more accessible to optimize the training process towards the final segmentation target without degrading the performance. IoU metric is used for the evaluation on the test dataset. We compared our designed network performance with the current state of the art segmentation networks on our RGB tongue dataset which was captured by the developed TIAS system for tongue diagnosis. Our designed network reduced 80 percentage parameters compared to the most widely used U-Net in medical image segmentation and achieved similar or better performance. Any terminal with limited storage which is needed a segment of RGB image can refer to our designed PPAN.

This paper proposes an anomaly prediction method based on k-means clustering that assumes embedded devices with memory constraints. With this method, by checking control system behavior in detail using k-means clustering, it is possible to predict anomalies. However, continuing clustering is difficult because data accumulate in memory similar to existing k-means clustering method, which is problematic for embedded devices with low memory capacity. Therefore, we also propose k-means clustering to continue clustering for infinite stream data. The proposed k-means clustering method is based on online k-means clustering of sequential processing. The proposed k-means clustering method only stores data required for anomaly prediction and releases other data from memory. Due to these characteristics, the proposed k-means clustering realizes that anomaly prediction is performed by reducing memory consumption. Experiments were performed with actual data of control system for anomaly prediction. Experimental results show that the proposed anomaly prediction method can predict anomaly, and the proposed k-means clustering can predict anomalies similar to standard k-means clustering while reducing memory consumption. Moreover, the proposed k-means clustering demonstrates better results of anomaly prediction than existing online k-means clustering.

Hadoop, a distributed processing framework for big-data, is now widely used for multimedia processing. However, when processing video data from a Hadoop distributed file system (HDFS), unnecessary network traffic is generated due to an inefficient HDFS block slice policy for picture frames in video files. We propose a new block replication policy to solve this problem and compare the newly proposed HDFS with the original HDFS via extensive experiments. The proposed HDFS reduces network traffic, and increases locality between processing cores and file locations.