In order to further reduce the transmission rate of multi-channel satellite broadcast signals, whose carrier-to-noise ratio (CNR fluctuates due to rainfall attenuation, we propose a novel digitized radio-over-fiber (DRoF) -based optical re-transmission system based on adaptive combination compression for ultra-high definition (UHD) broadcasting satellite (BS)/communications satellite (CS) broadcast signals. The proposed system reduces the optical re-transmission rate of BS/CS signals as much as possible while handling input CNR fluctuations. Therefore, the transmission rate of communication signals in time-division multiplexing (TDM) transmission is ensured, and network sharing of communication signals and broadcast signals via passive optical network (PON) is realized. Based on the ITU-R P.618-13 prediction model, an experimental evaluation is performed using estimates of the long-term statistics of attenuation due to rainfall. The attenuation is evaluated as a percentage of the time that long-term re-transmission service is available. It is shown that the proposed system is able to accommodate a wide range of rainfall attenuation and achieve a 99.988% time percentage for the duration of service provision. In order to show the rate reduction effect of the proposed system, the quantization bit reduction effect as a function of the input CNR, which depends on rainfall attenuation, is experimentally confirmed. Experiments show that service operation time of 99.978% can be achieved by 3-bit transmission. This means a 62.5% reduction in transmission rate is realized compared to conventional fixed quantization. Furthermore, the average quantization bit number in our system for service operation times is 3.000, indicating that most service operation times are covered by just 3-bit transmission.

Given a set of n disjoint intervals on a line and an integer k, we want to find k points in the intervals so that the minimum pairwise distance of the k points is maximized. Intuitively, given a set of n disjoint time intervals on a timeline, each of which is a time span we are allowed to check something, and an integer k, which is the number of times we will check something, we plan k checking times so that the checks occur at equal time intervals as much as possible, that is, we want to maximize the minimum time interval between the k checking times. We call the problem the k-dispersion problem on intervals. If we need to choose exactly one point in each interval, so k=n, and the disjoint intervals are given in the sorted order on the line, then two O(n) time algorithms to solve the problem are known. In this paper we give the first O(n) time algorithm to solve the problem for any constant k. Our algorithm works even if the disjoint intervals are given in any (not sorted) order. If the disjoint intervals are given in the sorted order on the line, then, by slightly modifying the algorithm, one can solve the problem in O(log n) time. This is the first sublinear time algorithm to solve the problem. Also we show some results on the k-dispersion problem on disks, including an FPTAS.

Generative Adversarial Networks (GANs) are one of the most successful learning principles of generative models and were wildly applied to many generation tasks. In the beginning, the gradient penalty (GP) was applied to enforce the discriminator in GANs to satisfy Lipschitz continuity in Wasserstein GAN. Although the vanilla version of the gradient penalty was further modified for different purposes, seeking a better equilibrium and higher generation quality in adversarial learning remains challenging. Recently, DRAGAN was proposed to achieve the local linearity in a surrounding data manifold by applying the noised gradient penalty to promote the local convexity in model optimization. However, we show that their approach will impose a burden on satisfying Lipschitz continuity for the discriminator. Such conflict between Lipschitz continuity and local linearity in DRAGAN will result in poor equilibrium, and thus the generation quality is far from ideal. To this end, we propose a novel approach to benefit both local linearity and Lipschitz continuity for reaching a better equilibrium without conflict. In detail, we apply our synchronized activation function in the discriminator to receive a particular form of noised gradient penalty for achieving local linearity without losing the property of Lipschitz continuity in the discriminator. Experimental results show that our method can reach the superior quality of images and outperforms WGAN-GP, DiracGAN, and DRAGAN in terms of Inception Score and Fréchet Inception Distance on real-world datasets.

An interpretation method of inversion phenomena is newly proposed for backward transient scattered field components for both E- and H-polarizations when an ultra-wideband (UWB) pulse wave radiated from a line source is incident on a two-dimensional metal cylinder covered with a lossless dielectric medium layer (coated metal cylinder). A time-domain (TD) asymptotic solution, which is referred to as a TD saddle point technique (TD-SPT), is derived by applying the SPT in evaluating a backward transient scattered field which is expressed by an integral form. The TD-SPT is represented by a combination of a direct geometric optical ray (DGO) and a reflected GO (RGO) series, thereby being able to extract and calculate any backward transient scattered field component from a response waveform. The TD-SPT is useful in understanding the response waveform of a backward transient scattered field by a coated metal cylinder because it can give us the peak value and arrival time of any field component, namely DGO and RGO components, and interpret analytically inversion phenomenon of any field component. The accuracy, validity, and practicality of the TD-SPT are clarified by comparing it with two kinds of reference solutions.

Register pushdown system (RPDS) is an extension of pushdown system (PDS) that has registers for dealing with data values. An LTL model checking method for RPDS with regular valuations has been proposed in previous work; however, the method requires the register automata (RA) used for defining a regular valuation to be backward-deterministic. This paper proposes another approach to the same problem, in which the model checking problem for RPDS is reduced to that problem for PDS by constructing a PDS bisimulation equivalent to a given RPDS. This construction is simpler than the previous model checking method and does not require RAs deterministic or backward-deterministic, and the bisimulation equivalence clearly guarantees the correctness of the reduction. On the other hand, the proposed method requires every RPDS (and RA) to have the freshness property, in which whenever the RPDS updates a register with a data value not stored in any register or the stack top, the value should be fresh. This paper also shows that the model checking problem with regular valuations defined by general RA is undecidable, and thus the freshness constraint is essential in the proposed method.

In the online removable knapsack problem, a sequence of items, each labeled with its value and its size, is given one by one. At each arrival of an item, a player has to decide whether to put it into a knapsack or to discard it. The player is also allowed to discard some of the items that are already in the knapsack. The objective is to maximize the total value of the knapsack. Iwama and Taketomi gave an optimal algorithm for the case where the value of each item is equal to its size. In this paper we consider a case with an additional constraint that the capacity of the knapsack is a positive integer N and that the sizes of items are all integral. For each positive integer N, we design an algorithm and prove its optimality. It is revealed that the competitive ratio is not monotonic with respect to N.

This paper proposes a novel interference cancellation technique that prevents radio receivers from degrading due to periodic interference signals caused by electromagnetic waves emitted from high power circuits. The proposed technique cancels periodic interference signals in the frequency domain, even if the periodic interference signals drift in the time domain. We propose a drift estimation based on a super resolution technique such as ESPRIT. Moreover, we propose a sequential drift estimation to enhance the drift estimation performance. The proposed technique employs a linear filter based on the minimum mean square error criterion with assistance of the estimated drifts for the interference cancellation. The performance of the proposed technique is confirmed by computer simulation. The proposed technique achieves a gain of more than 40dB at the higher frequency part in the band. The proposed canceler achieves such superior performance, if the parameter sets are carefully selected. The proposed sequential drift estimation relaxes the parameter constraints, and enables the proposed cancellation to achieve the performance upper bound.

The performance of conventional direction of arrival (DOA) methods is susceptible to the uncertainty of acoustic velocity in the underwater environment. To solve this problem, an underwater DOA estimation method with L-shaped array for wide-band signals under unknown acoustic velocity is proposed in this paper. The proposed method refers to the idea of incoherent signal subspace method and Root-MUSIC to obtain two sets of average roots corresponding to the subarray of the L-shaped array. And the geometric relationship between two vertical linear arrays is employed to derive the expression of DOA estimation with respect to the two average roots. The acoustic velocity variable in the DOA estimation expression can be eliminated in the proposed method. The simulation results demonstrate that the proposed method is more accurate and robust than other methods in an unknown acoustic velocity environment.

In this paper, we address the problem of finding a representation of a subtree distance, which is an extension of a tree metric. We show that a minimal representation is uniquely determined by a given subtree distance, and give an O(n2) time algorithm that finds such a representation, where n is the size of the ground set. Since a lower bound of the problem is Ω(n2), our algorithm achieves the optimal time complexity.

This paper investigates the joint problem of user association and spectrum allocation in satellite-terrestrial integrated networks (STINs), where a low earth orbit (LEO) satellite access network cooperating with terrestrial networks constitutes a heterogeneous network, which is beneficial in terms of both providing seamless coverage as well as improving the backhaul capacity for the dense network scenario. However, the orbital movement of satellites results in the dynamic change of accessible satellites and the backhaul capacities. Moreover, spectrum sharing may be faced with severe co-channel interferences (CCIs) caused by overlapping coverage of multiple access points (APs). This paper aims to maximize the total sum rate considering the influences of the dynamic feature of STIN, backhaul capacity limitation and interference management. The optimization problem is then decomposed into two subproblems: resource allocation for terrestrial communications and satellite communications, which are both solved by matching algorithms. Finally, simulation results show the effectiveness of our proposed scheme in terms of STIN's sum rate and spectrum efficiency.

This letter studies a biobjective optimization problem in binary associative memories characterized by ternary connection parameters. First, we introduce a condition of parameters that guarantees storage of any desired memories and suppression of oscillatory behavior. Second, we define a biobjective problem based on two objectives that evaluate uniform stability of desired memories and sparsity of connection parameters. Performing precise numerical analysis for typical examples, we have clarified existence of a trade-off between the two objectives.

The altered fingerprints help criminals escape from police and cause great harm to the society. In this letter, an altered fingerprint detection method is proposed. The method is constructed by two deep convolutional neural networks to train the time-domain and frequency-domain features. A spectral attention module is added to connect two networks. After the extraction network, a feature fusion module is then used to exploit relationship of two network features. We make ablation experiments and add the module proposed in some popular architectures. Results show the proposed method can improve the performance of altered fingerprint detection compared with the recent neural networks.

To accommodate an increasing amount of traffic efficiently, elastic optical networks (EON) that can use optical spectrum resources flexibly have been studied. We implement multi-path routing in case we cannot allocate the spectrum with single-path routing. However, multi-path routing requires more guard bands to avoid interference between two adjacent optical paths when compared with single-path routing in EON. A multi-path routing algorithm with traffic grooming technology has been proposed. The researchers assumed that a uniform modulation level was adopted, and so they did not consider the impact of path length on the resources needed. In this paper, we propose a multi-path routing method with traffic grooming considering path lengths. Our proposed method establishes an optical multi-path considering path length, fiber utilization, and the use of traffic grooming. Simulations show we can decrease the call-blocking probability by approximately 24.8% in NSFNET. We also demonstrate the effectiveness of traffic grooming and the improvement in the utilization ratio of optical spectrum resources.

This paper presents an improved YOLOv3 network, named MSFF-YOLOv3, for precisely detecting variable surface defects of aluminum profiles in practice. First, we introduce a larger prediction scale to provide detailed information for small defect detection; second, we design an efficient attention-guided block to extract more features of defects with less overhead; third, we design a bottom-up pyramid and integrate it with the existing feature pyramid network to construct a twin-tower structure to improve the circulation and fusion of features of different layers. In addition, we employ the K-median algorithm for anchor clustering to speed up the network reasoning. Experimental results showed that the mean average precision of the proposed network MSFF-YOLOv3 is higher than all conventional networks for surface defect detection of aluminum profiles. Moreover, the number of frames processed per second for our proposed MSFF-YOLOv3 could meet real-time requirements.

Integral cryptanalysis is one of the most powerful attacks on symmetric key block ciphers. Attackers preliminarily search integral characteristics of a target cipher and use them to perform the key recovery attack. Todo proposed a novel technique named the bit-based division property to find integral characteristics. Xiang et al. extended the Mixed Integer Linear Programming (MILP) method to search integral characteristics of lightweight block ciphers based on the bit-based division property. In this paper, we apply these techniques to the symmetric key block cipher KASUMI which was developed by modifying MISTY1. As a result, we found new 4.5-round characteristics of KASUMI for the first time. We show that 7-round KASUMI is attackable with 263 data and 2120 encryptions.

Although end-to-end based speech recognition research for Mandarin-English code-switching has attracted increasing interests, it remains challenging due to data scarcity. Meta-learning approach is popular with low-resource modeling using high-resource data, but it does not make full use of low-resource code-switching data. Therefore we propose a two-fold cross-validation training framework combined with meta-learning approach. Experiments on the SEAME corpus demonstrate the effects of our method.

The recurrent neural network (RNN) has been used in audio and speech processing, such as language translation and speech recognition. Although RNN-based architecture can be applied to speech synthesis, the long computing time is still the primary concern. This research proposes a fast gated recurrent neural network, a fast RNN-based architecture, for speech synthesis based on the minimal gated unit (MGU). Our architecture removes the unit state history from some equations in MGU. Our MGU-based architecture is about twice faster, with equally good sound quality than the other MGU-based architectures.

The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.

Deep unfolding is a promising deep-learning technique, whose network architecture is based on expanding the recursive structure of existing iterative algorithms. Although deep unfolding realizes convergence acceleration, its theoretical aspects have not been revealed yet. This study details the theoretical analysis of the convergence acceleration in deep-unfolded gradient descent (DUGD) whose trainable parameters are step sizes. We propose a plausible interpretation of the learned step-size parameters in DUGD by introducing the principle of Chebyshev steps derived from Chebyshev polynomials. The use of Chebyshev steps in gradient descent (GD) enables us to bound the spectral radius of a matrix governing the convergence speed of GD, leading to a tight upper bound on the convergence rate. Numerical results show that Chebyshev steps numerically explain the learned step-size parameters in DUGD well.

AONT (All-or-Nothing Transform) is a kind of (n, n)-threshold secret sharing scheme that distributes a message m into a set of n shares such that the message m can be reconstructed if and only if n shares are collected. At CRYPTO 2000, Desai proposed a simple and faster AONT based on the CTR mode of encryption (called CTRT) and proved its security in the ideal cipher model. Though AES-128, whose key length k = 128 and block length l = 128, can be used in CTRT as a block cipher, AES-256 and AES-192 cannot be used due to its intrinsic restriction of k ≤ l. In this paper, we propose an extended CTRT (for short, XCTRT) suitable for AES-256. By thoroughly evaluating all the tricky cases, we prove that XCTRT is secure in the ideal cipher model under the same CTRT security definition. Also, we discuss the security result of XCTRT in concrete parameter settings. For more flexibility of key length, we propose a variant of XCTRT dealing with l