In this study, we aim to improve the performance of audio source separation for monaural mixture signals. For monaural audio source separation, semisupervised nonnegative matrix factorization (SNMF) can achieve higher separation performance by employing small supervised signals. In particular, penalized SNMF (PSNMF) with orthogonality penalty is an effective method. PSNMF forces two basis matrices for target and nontarget sources to be orthogonal to each other and improves the separation accuracy. However, the conventional orthogonality penalty is based on an inner product and does not affect the estimation of the basis matrix properly because of the scale indeterminacy between the basis and activation matrices in NMF. To cope with this problem, a new PSNMF with cosine similarity between the basis matrices is proposed. The experimental comparison shows the efficacy of the proposed cosine similarity penalty in supervised audio source separation.

Recently, the performances of discriminative correlation filter (CF) trackers are getting better and better in visual tracking. In this paper, we propose spatial-temporal regularization with precise state estimation based on discriminative correlation filter (STPSE) in order to achieve more significant tracking performance. First, we consider the continuous change of the object state, using the information from the previous two filters for training the correlation filter model. Here, we train the correlation filter model with the hand-crafted features. Second, we introduce update control in which average peak-to-correlation energy (APCE) and the distance between the object locations obtained by HOG features and hand-crafted features are utilized to detect abnormality of the state around the object. APCE and the distance indicate the reliability of the filter response, thus if abnormality is detected, the proposed method does not update the scale and the object location estimated by the filter response. In the experiment, our tracker (STPSE) achieves significant and real-time performance with only CPU for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).

At present, the application of different types of memristors in electronics is being deeply studied. Given the nonlinearity characterizing memristors, a circuit with memristors cannot be treated by classical circuit analysis. In this paper, memristor is equivalent to a nonlinear dynamic system composed of linear dynamic system and nonlinear static system by Volterra series. The nonlinear transfer function of memristor is derived. In the complex frequency domain, the n-order complex frequency response of memristor is established by multiple Laplace transform, and the response of MLC parallel circuit is taken as an example to verify. Theoretical analysis shows that the complex frequency domain analysis method of memristor transforms the problem of solving nonlinear circuit in time domain into n times complex frequency domain analysis of linear circuit, which provides an idea for nonlinear dynamic system analysis.

Combinatorial testing is an effective testing technique for detecting faults in a software or hardware system with multiple factors using combinatorial methods. By performing a test, which is an assignment of possible values to all the factors, and verifying whether the system functions as expected (pass) or not (fail), the presence of faults can be detected. The failures of the tests are possibly caused by combinations of multiple factors assigned with specific values, called faulty interactions. Martínez et al. [1] proposed the first deterministic adaptive algorithm for discovering faulty interactions involving at most two factors where each factor has two values, for which graph representations are adopted. In this paper, we improve Martínez et al.'s algorithm by an adaptive algorithmic approach for discovering faulty interactions in the so-called “non-2-locatable” graphs. We show that, for any system where each “non-2-locatable factor-component” involves two faulty interactions (for example, a system having at most two faulty interactions), our improved algorithm efficiently discovers all the faulty interactions with an extremely low mistaken probability caused by the random selection process in Martínez et al.'s algorithm. The effectiveness of our improved algorithm are revealed by both theoretical discussions and experimental evaluations.

In this paper, we study the number of failed components in a consecutive-k-out-of-n:G system. The distributions and expected values of the number of failed components when system is failed or working at a particular time t are evaluated. We also apply them to the optimization problems concerned with the optimal number of components and the optimal replacement time. Finally, we present the illustrative examples for the expected number of failed components and give the numerical results for the optimization problems.

The NIST post-quantum project intends to standardize cryptographic systems that are secure against attacks by both quantum and classical computers. One of these cryptographic systems is NewHope that is a RING-LWE based key exchange scheme. The NewHope Key Encapsulation Method (KEM) allows to establish an encapsulated (secret) key shared by two participants. This scheme defines a private key that is used to encipher a random shared secret and the private key enables the deciphering. This paper presents Fault Information Leakage attacks, using conventional personal computers, if the attacked participant, say Bob, reuses his public key. This assumption is not so strong since reusing the pair (secret, public) keys saves Bob's device computing cost when the public global parameter is not changed. With our result we can conclude that, to prevent leakage, Bob should not reuse his NewHope secret and public keys because Bob's secret key can be retrieved with only 2 communications. We also found that Bob's secret keys can be retrieved for NewHopeToy2, NewHopeToy1 and NewHopeLudicrous with 1, 2, and 3 communications, respectively.

Side-channel analysis is one of the most investigated hardware Trojan detection approaches. However, nearly all the side-channel analysis approaches require golden chips for reference, which are hard to obtain actually. Besides, majority of existing Trojan detection algorithms focus on the data similarity and ignore the Trojan misclassification during the detection. In this paper, we propose a cost-sensitive golden chip-free hardware Trojan detection framework, which aims to minimize the probability of Trojan misclassification during the detection. The post-layout simulation data of voltage variations at different process corners is utilized as a golden reference. Further, a classification algorithm based on the combination of principal component analysis and Naïve bayes is exploited to identify the existence of hardware Trojan with a minimum misclassification risk. Experimental results on ASIC demonstrate that the proposed approach improves the detection accuracy ratio compared with the three detection algorithms and distinguishes the Trojan with only 0.27% area occupies even under ±15% process variations.

Quantum combinatorial designs are gaining popularity in quantum information theory. Quantum Latin squares can be used to construct mutually unbiased maximally entangled bases and unitary error bases. Here we present a general method for constructing quantum Latin arrangements from irredundant orthogonal arrays. As an application of the method, many new quantum Latin arrangements are obtained. We also find a sufficient condition such that the improved quantum orthogonal arrays [10] are equivalent to quantum Latin arrangements. We further prove that an improved quantum orthogonal array can produce a quantum uniform state.

When collecting images, owing to the influence of shooting equipment, shooting environment, and other factors, often low-illumination images with insufficient exposure are obtained. For low-illumination images, it is necessary to improve the contrast. In this paper, a digital color image contrast enhancement method based on luminance weight adjustment is proposed. This method improves the contrast of the image and maintains the detail and nature of the image. In the proposed method, the illumination of the histogram equalization image and the adaptive gamma correction with weighted distribution image are adjusted by the luminance weight of w₁ to obtain a detailed image of the bright areas. Thereafter, the suppressed multi-scale retinex (MSR) is used to process the input image and obtain a detailed image of the dark areas. Finally, the luminance weight w₂ is used to adjust the illumination component of the detailed images of the bright and dark areas, respectively, to obtain the output image. The experimental results show that the proposed method can enhance the details of the input image and avoid excessive enhancement of contrast, which maintains the naturalness of the input image well. Furthermore, we used the discrete entropy and lightness order error function to perform a numerical evaluation to verify the effectiveness of the proposed method.

Magnetic fields are often utilized for position sensing of mobile devices. In typical sensing systems, multiple sensors are used to detect magnetic fields generated by target devices. To determine the positions of the devices, magnetic-field data detected by the sensors must be converted to device-position data. The data conversion is not trivial because it is a nonlinear inverse problem. In this study, we propose a machine-learning approach suitable for data conversion required in the magnetic-field-based position sensing of target devices. In our approach, two different sets of training data are used. One of the training datasets is composed of raw data of magnetic fields to be detected by sensors. The other set is composed of logarithmically represented data of the fields. We can obtain two different predictor functions by learning with these training datasets. Results show that the prediction accuracy of the target position improves when the two different predictor functions are used. Based on our simulation, the error of the target position estimated with the predictor functions is within 10cm in a 2m × 2m × 2m cubic space for 87% of all the cases of the target device states. The computational time required for predicting the positions of the target device is 4ms. As the prediction method is accurate and rapid, it can be utilized for the real-time tracking of moving objects and people.

Numerous variable tap-length algorithms can be found in some literature and few strategies are derived from a basic theoretical formula. Thus, some algorithms lack of theoretical depth and their performance are unstable. In view of this point, the novel variable tap-length algorithm which is based on the mixed error cost function is presented in this letter. By analyzing the mixed expectation of the prior and the posterior error, the novel variable tap-length strategy is derived. The proposed algorithm has a more valid proximity to the optimal tap-length and a good convergence ability by the performance analysis. It can solve many deficiencies comprising large fluctuations of the tap-length, the high complexity and the weak steady-state ability. Simulation results demonstrate that the proposed algorithm equips good performance.

A robust saliency detection approach for images with a complex background is proposed. The absorbing Markov chain integrating low-level, mid-level and high-level cues dynamically evolves by using the similarity between pixels to detect saliency objects. The experimental results show that the proposed algorithm has advantages in saliency detection, especially for images with a chaotic background or low contrast.

In the state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection (FDI) attack. In this letter, to enforce the security of power networks, we propose a method of detecting an FDI attack. In the proposed method, an FDI attack is detected by randomly choosing sensors used in the state estimation. The effectiveness of the proposed method is presented by two examples including the IEEE 14-bus system.