This letter discusses the prediction of the time-varying bit error rate (BER) for a transmitting channel using recent transmissions and retransmissions. Depending on the predicted BER, we propose a maximum frame size control to improve the goodput in wireless networks. It is shown, using simulation, that when the maximum frame size is controlled relative to the time-varying BER the goodput of the network is improved.

We propose a new intelligent blind source separation algorithm for the mixture of sub-Gaussian and super-Gaussian sources. The algorithm consists of an update equation of the separating matrix and an adjustment equation of nonlinear functions. To verify the validity of the proposed algorithm, we compare the proposed algorithm with extant methods.

In this paper, the previous definition of the Reverse Jacket matrix (RJM) is revised and generalized. In particular, it is shown that the inverse of the RJM can be obtained easily by a constructive approach similar to that used for the RJM itself. As new results, some useful properties of RJMs, such as commutativity and the Hamiltonian symmetry appearing in half the blocks of a RJM, are shown, and also 1-D fast Reverse Jacket transform (FRJT) is presented. The algorithm of the FRJT is remarkably efficient than that of the center-weighted Hadamard transform (CWHT). The FRJT is extended in terms of the Kronecker products of the Hadamard matrix. The 1-D FRJT is applied to the discrete Fourier transform (DFT) with order 4, and the N-point DFT can be expressed in terms of matrix decomposition by using 4 4 FRJT.

In this paper a cyclic place-timed controlled marked graph (PTCMG), which is an extended class of a cyclic controlled marked graph (CMG), is presented as a model of discrete event systems (DESs). In a PTCMG, time constraints are attached to places instead of transitions. The time required for a marked place to be marked again is represented in terms of time constraints attached to places. The times required for an unmarked place to be marked under various controls, are calculated. The necessary and sufficient condition for a current marking to be in the admissible marking set with respect to the given forbidden condition is provided, as is the necessary and sufficient condition for a current marking to be out of the admissible marking set with respect to the forbidden condition in one transition. A maximally permissive state feedback control is synthesized in a PTCMG to guarantee a larger admissible marking set than a CMG for most forbidden state problems. Practical applications are illustrated for a railroad crossing problem and an automated guided vehicle (AGV) coordination problem in a flexible manufacturing facility.

We propose a rootkit installation method inside a GPU kernel execution process which works through GPU context manipulation. In GPU-based applications such as deep learning computations and cryptographic operations, the proposed method uses the feature by which the execution flow of the GPU kernel obeys the GPU context information in GPU memory. The proposed method consists of two key ideas. The first is GPU code manipulation, which is able to hijack the execution flow of the original GPU kernel to execute an injected payload without affecting the original GPU computation result. The second is a self-page-table update execution during which the GPU kernel updates its page table to access any location in system memory. After the installation, the malicious payload is executed only in the GPU kernel, and any no evidence remains in system memory. Thus, it cannot be detected by conventional rootkit detection methods.

Deep neural networks show good performance in image recognition, speech recognition, and pattern analysis. However, deep neural networks also have weaknesses, one of which is vulnerability to poisoning attacks. A poisoning attack reduces the accuracy of a model by training the model on malicious data. A number of studies have been conducted on such poisoning attacks. The existing type of poisoning attack causes misrecognition by one classifier. In certain situations, however, it is necessary for multiple models to misrecognize certain data as different specific classes. For example, if there are enemy autonomous vehicles A, B, and C, a poisoning attack could mislead A to turn to the left, B to stop, and C to turn to the right simply by using a traffic sign. In this paper, we propose a multi-targeted poisoning attack method that causes each of several models to misrecognize certain data as a different target class. This study used MNIST and CIFAR10 as datasets and Tensorflow as a machine learning library. The experimental results show that the proposed scheme has a 100% average attack success rate on MNIST and CIFAR10 when malicious data accounting for 5% of the training dataset have been used for training.

This letter presents parametric uncertainty bounds (PUBs) for stabilizing receding horizon H∞ control (RHHC). The proposed PUBs are obtained easily by solving convex optimization problems represented by linear matrix inequalities (LMIs). We show, by numerical example, that the RHHC can guarantee a H∞ norm bound for a larger class of uncertain systems than conventional infinite horizon H∞ control (IHHC).

Let V(φ) be a shift invariant subspace of L2(R) with a Riesz or frame generator φ(t). We take φ(t) suitably so that the regular sampling expansion : f(t) = f(n)S(t-n) holds on V(φ). We then find conditions on the generator φ(t) and various bounds of the perturbation {δ n }n∈Z under which an irregular sampling expansion: f(t) = f(n+ δn)Sn(t) holds on V(φ). Some illustrating examples are also provided.

Shot transition detection is a core technology in video browsing, indexing systems and information retrieval. In this paper we propose a dissolve detection algorithm using the characteristics of edge in MPEG compressed video. Using the intensity change information of edge pixels obtained by Sobel edge detector, we detect the location of a dissolve and its precise duration. We also present a new reliable method to eliminate the false dissolves. The proposed algorithm is tested in various types of videos, and the experimental results show that the proposed algorithm is effective and robust.

We propose a blind source separation algorithm for the mixture of finite alphabet sources where sensors are less than sources. The algorithm consists of an update equation of an estimated mixing matrix and enumeration of the inferred sources. We present the bound of a step size for the stability of the algorithm and two methods of assignment of the initial point of the estimated mixing matrix. Simulation results verify the proposed algorithm.

We present a multi-channel sampling expansion for signals with selectively tiled band-region. From this we derive an oversampling expansion for any bandpass signal, and show that any finitely many missing samples from two-channel oversampling expansion can always be uniquely recovered. In addition, we find a sufficient condition under which some infinitely many missing samples can be recovered. Numerical stability of the recovery process is also discussed in terms of the oversampling rate and distribution of the missing samples.

Deep neural networks (DNNs) perform well for image recognition, speech recognition, and pattern analysis. However, such neural networks are vulnerable to adversarial examples. An adversarial example is a data sample created by adding a small amount of noise to an original sample in such a way that it is difficult for humans to identify but that will cause the sample to be misclassified by a target model. In a military environment, adversarial examples that are correctly classified by a friendly model while deceiving an enemy model may be useful. In this paper, we propose a method for generating a selective adversarial example that is correctly classified by a friendly gait recognition system and misclassified by an enemy gait recognition system. The proposed scheme generates the selective adversarial example by combining the loss for correct classification by the friendly gait recognition system with the loss for misclassification by the enemy gait recognition system. In our experiments, we used the CASIA Gait Database as the dataset and TensorFlow as the machine learning library. The results show that the proposed method can generate selective adversarial examples that have a 98.5% attack success rate against an enemy gait recognition system and are classified with 87.3% accuracy by a friendly gait recognition system.

This letter presents robustness bounds (RBs) for receding horizon controls (RHCs) of uncertain systems. The proposed RBs are obtained easily by solving convex problems represented by linear matrix inequalities (LMIs). We show, by numerical examples, that the RHCs can guarantee robust stabilization for a larger class of uncertain systems than conventional linear quadratic regulators (LQRs).

Deep neural networks show good performance in image recognition, speech recognition, and pattern analysis. However, deep neural networks show weaknesses, one of which is vulnerability to backdoor attacks. A backdoor attack performs additional training of the target model on backdoor samples that contain a specific trigger so that normal data without the trigger will be correctly classified by the model, but the backdoor samples with the specific trigger will be incorrectly classified by the model. Various studies on such backdoor attacks have been conducted. However, the existing backdoor attack causes misclassification by one classifier. In certain situations, it may be necessary to carry out a selective backdoor attack on a specific model in an environment with multiple models. In this paper, we propose a multi-model selective backdoor attack method that misleads each model to misclassify samples into a different class according to the position of the trigger. The experiment for this study used MNIST and Fashion-MNIST as datasets and TensorFlow as the machine learning library. The results show that the proposed scheme has a 100% average attack success rate for each model while maintaining 97.1% and 90.9% accuracy on the original samples for MNIST and Fashion-MNIST, respectively.

In this paper, a new method is proposed for solving forbidden state problems in non-ordinary controlled Petri nets (NCPNs) with uncontrollable transitions. Using a precedence subnet and a boundary subnet with decision-free properties, the behavior of markings are analyzed structurally. An efficient algorithm is presented for calculating the number of total tokens in forbidden places reachable from a marking. This paper derives necessary and sufficient conditions for identifying admissible markings and boundary markings in terms of the precedence subnet and the boundary subnet.

This paper presents an efficient method to derive the first passage time of an extended stochastic Petri net by simple algebraic operations. The reachability graph is derived from an extended stochastic Petri net, and then converted to a timed stochastic state machine which is a semi-Markov process. The mean and the variance of the first passage time are derived by algebraic manipulations with the mean and the variance of the transition time, and the transition probability for each transition in the state machine model. For the derivation, three reduction rules are introduced on the transition trajectories in a well-formed regular expression. An efficient algorithm is provided to automate the suggested method.

In this paper, a token-controlled network with exhaustive service strategy is analyzed. The mean and variance of service time of a station, and the mean token rotation time on the network are derived under the condition that the buffer capacity of each station is individually finite. For analysis, an extended stochastic Petri-net model of a station is presented. Then, by analyzing the model, the mean service time of a station and the mean token rotation time are derived, as functions of the given network parameters such as the total number of stations on the network, the arrival rate of frames, the transmission rate of frames, and the buffer capacity. The variance of service time of a station is also derived. By examining derived results, it is shown that they exactly describe the actual operations of the network. In addition, computer simulations with sufficient confidence intervals help to validate the results.

In this paper, the performance of IEEE 802.11b WLAN under the interference of IEEE 802.15.4 WPAN is analyzed. An analytic model for the coexistence of IEEE 802.15.4 and IEEE 802.11b is presented. Packet error rate, average transmission time, and throughput are evaluated.

In this paper, we propose a selective membership inference attack method that determines whether certain data corresponding to a specific class are being used as training data for a machine learning model or not. By using the proposed method, membership or non-membership can be inferred by generating a decision model from the prediction of the inference models and training the confidence values for the data corresponding to the selected class. We used MNIST as an experimental dataset and Tensorflow as a machine learning library. Experimental results show that the proposed method has a 92.4% success rate with 5 inference models for data corresponding to a specific class.

We first find simple characterizations of $rac{1}{N} mathbb{Z}$-invariance of arbitrary principal shift-invariant space $V(phi)$. Then we find several equivalent conditions for $V(phi)$ to admit periodic oversampling for a class of continuous frame generators $phi$. In particular, when $phi$ is band-limited and $hat{phi}$ is piecewise continuous, we find very simple and general sufficient conditions for $V(phi)$ to admit periodic oversampling, which involve the extra invariance of $V(phi)$, together with an illustrating example.