Pinning control of multi-agent systems is a method that the external control input is added to some agents (pinning nodes), e.g., leaders. By the external control input, consensus to a certain target value and faster consensus are achieved. In this paper, we propose a new method of self-triggered predictive pinning control for the consensus problem. Self-triggered control is a method that both the control input and the next update time are calculated. Using self-triggered control, it is expected that the communication cost can be reduced. First, a new finite-time optimal control problem used in self-triggered control is formulated, and its solution method is derived. Next, as an on-line algorithm, two methods, i.e., the multi-hop communication-based method and the observer-based method are proposed. Finally, numerical examples are presented.

Satellite communications (SATCOM) systems play important roles in wireless communication systems. In the future, they will be required to accommodate rapidly increasing communication requests from various types of users. Therefore, we propose a framework for efficient resource management in large-scale SATCOM systems that integrate multiple satellites. Such systems contain hundreds of thousands of communication satellites, user terminals, and gateway stations; thus, our proposed framework enables simpler and more reliable communication between users and satellites. To manage and control this system efficiently, we formulate an optimization problem that designs the network structure and allocates communication resources for a large-scale SATCOM system. In this mixed integer programming problem, we allow the cost function to be a combination of various factors so that SATCOM operators can design the network according to their individual management strategies. These factors include the total allocated bandwidth to users, the number of satellites and gateway stations to be used, and the number of total satellite handovers. Our numerical simulations show that the proposed management strategy outperforms a conventional strategy in which a user can connect to only one specific satellite determined in advance. Furthermore, we determine the effect of the number of satellites in the system on overall system performance.

Register-transfer-level (RTL) information is hardly available for hardware Trojan detection. In this paper, four RTL Trojan features related to branching statement are proposed. The Minimum Redundancy Maximum Relevance (mRMR) feature selection is applied to the proposed Trojan features to determine the recommended feature combinations. The feature combinations are then tested using different machine learning concepts in order to determine the best approach for classifying Trojan and normal branches. The result shows that a Decision Tree classification algorithm with all the four proposed Trojan features can achieve an average true positive detection rate of 93.72% on unseen test data.

S-shaped nonlinearity is found in the electrical resistance-length relationship in an electroactive supercoiled polymer artificial muscle. The modulation of the electrical resistance is mainly caused by the change in the contact condition of coils in the artificial muscle upon deformation. A mathematical model based on logistic function fairly reproduces the experimental data of electrical resistance-length relationship.

Radio signals show small characteristic differences between radio transmitters resulted from their idiosyncratic hardware properties. Based on the parameters estimation of transmitter imperfections, a novel radiometric identification method is presented in this letter. The fingerprint features of the radio are extracted from the mismatches of the modulator and the nonlinearity of the power amplifier, and used to train a support vector machine classifier to identify the class label of a new data. Experiments on real data sets demonstrate the validation of this method.

Unlike black-box cryptography, an adversary in a white-box security model has full access to the implementation of the cryptographic algorithm. Thus, white-box implementation of cryptographic algorithms is more practical. Nevertheless, in recent years, there is no white-box implementation for public key cryptography. In this paper, we propose the first white-box implementation of the identity-based signature scheme in the IEEE P1363 standard. Our main idea is to hide the private key to multiple lookup tables, so that the private key cannot be leaked during the algorithm executed in the untrusted environment. We prove its security in both black-box and white-box models. We also evaluate the performance of our white-box implementations, in order to demonstrate utility for real-world applications.

Software-Defined Networking (SDN) enables flexible deployment and innovation of new networking applications by decoupling and abstracting the control and data planes. It has radically changed the concept and way of building and managing networked systems, and reduced the barriers to entry for new players in the service markets. It is considered to be a promising solution providing the scale and versatility necessary for IoT. However, SDN may also face many challenges, i.e., the centralized control plane would be a single point of failure. With the advent of blockchain technology, blockchain-based SDN has become an emerging architecture for securing a distributed network environment. Motivated by this, in this work, we summarize the generic framework of blockchain-based SDN, discuss security challenges and relevant solutions, and provide insights on the future development in this field.

RFID enable applications are ubiquitous in our society, especially become more and more important as IoT management rises. Meanwhile, the concern of security and privacy of RFID is also increasing. The pseudorandom number generator is one of the core primitives to implement RFID security. Therefore, it is necessary to design and implement a secure and robust pseudo-random number generator (PRNG) for current RFID tag. In this paper, we study the security of light-weight PRNGs for EPC Gen2 RFID tag which is an EPC Global standard. For this reason, we have analyzed and improved the existing research at IEEE TrustCom 2017 and proposed a model using external random numbers. However, because the previous model uses external random numbers, the speed has a problem depending on the generation speed of external random numbers. In order to solve this problem, we developed a pseudorandom number generator that does not use external random numbers. This model consists of LFSR, NLFSR and SLFSR. Safety is achieved by using nonlinear processing such as multiplication and logical multiplication on the Galois field. The cycle achieves a cycle longer than the key length by effectively combining a plurality of LFSR and the like. We show that our proposal PRNG has good randomness and passed the NIST randomness test. We also shows that it is resistant to identification attacks and GD attacks.

The conventional linear or tiled address maps can degrade performance and memory utilization when traffic patterns are not matched with an underlying address map. The address map is usually fixed at design time. Accordingly, it is difficult to adapt to given applications. Modern embedded system usually accommodates memory management units (MMUs). As a result, depending on virtual address patterns, the system can suffer from performance overheads due to page table walks. To alleviate this performance overhead, we propose to cluster and rearrange tiles to construct an MMU-aware configurable address map. To construct the clustered tiled map, the generic tile number remapping algorithm is presented. In the presented scheme, an address map is configured based on the adaptive dimensioning algorithm. Considering image processing applications, a design, an analysis, an implementation, and simulations are conducted. The results indicate the proposed method can improve the performance and the memory utilization with moderate hardware costs.

Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks. Since a trained layered neural network consists of a complex nonlinear relationship between large number of parameters, we failed to understand how they could achieve input-output mappings with a given data set. In this paper, we propose the non-negative task matrix decomposition method, which applies non-negative matrix factorization to a trained layered neural network. This enables us to decompose the inference mechanism of a trained layered neural network into multiple principal tasks of input-output mapping, and reveal the roles of hidden units in terms of their contribution to each principal task.

Under the framework of traditional power spectrum based feature extraction, in order to extract more discriminative information for playback attack detection, this paper proposes a feature by making use of deep neural network to describe the nonlinear relationship between power spectrum and discriminative information. Namely, constant-Q deep coefficients (CQDC). It relies on constant-Q transform, deep neural network and discrete cosine transform. In which, constant-Q transform is used to convert signal from the time domain into the frequency domain because it is a long-term transform that can provide more frequency detail, deep neural network is used to extract more discriminative information to discriminate playback speech from genuine speech and discrete cosine transform is used to decorrelate among the feature dimensions. ASVspoof 2017 corpus version 2.0 is used to evaluate the performance of CQDC. The experimental results show that CQDC outperforms the existing power spectrum obtained from constant-Q transform based features, and equal error can reduce from 19.18% to 51.56%. In addition, we found that discriminative information of CQDC hides in all frequency bins, which is different from commonly used features.

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.

In this paper, we introduce conditional decisions for enforcing forcible events in the decentralized supervisory control framework for timed discrete event systems. We first present sufficient conditions for the existence of a decentralized supervisor with conditional decisions. These sufficient conditions are weaker than the necessary and sufficient conditions for the existence of a decentralized supervisor without conditional decisions. We next show that the presented sufficient conditions are also necessary under the assumption that if the occurrence of the event tick, which represents the passage of one time unit, is illegal, then a legal forcible event that should be forced to occur uniquely exists. In addition, we develop a method for verifying the presented conditions under the same assumption.

This paper proposes statistical analysis of phase-only correlation functions between two signals with stochastic phase-spectra following bivariate circular probability distributions based on directional statistics. We give general expressions for the expectation and variance of phase-only correlation functions in terms of joint characteristic functions of the bivariate circular probability density function. In particular, if we assume bivariate wrapped distributions for the phase-spectra, we obtain exactly the same results between in case of a bivariate linear distribution and its corresponding bivariate wrapped distribution.

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.

This Letter explores the adaptive hybrid automatic repeat request (HARQ) using rate-compatible polar codes constructed with a common information set. The rate adaptation problem is formulated using Markov decision process and solved by a dynamic programming framework in a low-complexity way. Simulation verifies the throughput efficiency of the proposed adaptive HARQ.

This paper presents the excitation coefficient optimization of slot array antennas for increasing channel capacity in 2×2-mode two-dimensional ROM (rectangular coordinate orthogonal) transmission. Because the ROM transmission is for non-far region communication, the transmission between Tx (transmission) and Rx (reception) antennas increases when the antennas radiate beams inwardly. At first, we design the excitation coefficients of the slot arrays in order to enhance the transmission rate for a given transmission distance. Then, we fabricate monopulse corporate-feed waveguide slot array antennas that have the designed excitation amplitude and phase in the 60-GHz band for the 2×2-mode two-dimensional ROM transmission. The measured transmission between the fabricated Tx and Rx antennas increases at the given propagation distance and agrees with the simulation.

Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.

In this paper, we consider the design problem of an unknown-input observer for distributed network systems under the existence of communication delays. In the proposed method, each node estimates all states and calculates inputs from its own estimate. It is assumed that the controller of each node is given by an observer-based controller. When calculating each node, the input values of the other nodes cannot be utilized. Therefore, each node calculates alternative inputs instead of the unknown inputs of the other nodes. The alternative inputs are generated by own estimate based on the feedback controller of the other nodes given by the assumption. Each node utilizes these values instead of the unknown inputs when calculating the estimation and delay compensation. The stability of the estimation error of the proposed observer is proven by a Lyapunov-Krasovskii functional. The stability condition is given by a linear matrix inequality (LMI). Finally, the result of a numerical simulation is shown to verify the effectiveness of the proposed method.

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.