Noise is the most common type of image distortion affecting human visual perception. In this paper, we propose a no-reference image quality assessment (IQA) method for noisy images incorporating the features of entropy, gradient, and kurtosis. Specifically, image noise estimation is conducted in the discrete cosine transform domain based on skewness invariance. In the principal component analysis domain, kurtosis feature is obtained by statistically counting the significant differences between images with and without noise. In addition, both the consistency between the entropy and kurtosis features and the subjective scores are improved by combining them with the gradient coefficient. Support vector regression is applied to map all extracted features into an integrated scoring system. The proposed method is evaluated in three mainstream databases (i.e., LIVE, TID2013, and CSIQ), and the results demonstrate the superiority of the proposed method according to the Pearson linear correlation coefficient which is the most significant indicator in IQA.
For the use in low-power and lossy networks (LLNs) under complex and harsh communication conditions, the routing protocol for LLNs (RPL) standardized by the Internet Engineering Task Force is specially designed. To improve the performance of LLNs, we propose a novel context-aware RPL algorithm based on a triangle module operator (CAR-TMO). A novel composite context-aware routing metric (CA-RM) is designed, which synchronously evaluates the residual energy index, buffer occupancy ratio of a node, expected transmission count (ETX), delay, and hop count from a candidate parent to the root. CA-RM considers the residual energy index and buffer occupancy ratio of the candidate parent and its preferred parent in a recursive manner to reduce the effect of upstream parents, since farther paths are considered. CA-RM comprehensively uses the sum, mean, and standard deviation values of ETX and delay of links in a path to ensure a better performance. Moreover, in CAR-TMO, the membership function of each routing metric is designed. Then, a comprehensive membership function is constructed based on a triangle module operator, the membership function of each routing metric, and a comprehensive context-aware objective function. A novel mechanism for calculating the node rank and the mechanisms for preferred parent selection are proposed. Finally, theoretical analysis and simulation results show that CAR-TMO outperforms several state-of-the-art RPL algorithms in terms of the packet delivery ratio and energy efficiency.
Motivated by the inconvenience or even inability to explain the mathematics of the state space optimization of finite state machines (FSMs) in most existing results, we consider the problem by viewing FSMs as logical dynamic systems. Borrowing ideas from the concept of equilibrium points of dynamic systems in control theory, the concepts of t-equivalent states and t-source equivalent states are introduced. Based on the state transition dynamic equations of FSMs proposed in recent years, several mathematical formulations of t-equivalent states and t-source equivalent states are proposed. These can be analogized to the necessary and sufficient conditions of equilibrium points of dynamic systems in control theory and thus give a mathematical explanation of the optimization problem. Using these mathematical formulations, two methods are designed to find all the t-equivalent states and t-source equivalent states of FSMs. Further, two ways of reducing the state space of FSMs are found. These can be implemented without computers but with only pen and paper in a mathematical manner. In addition, an open question is raised which can further improve these methods into unattended ones. Finally, the correctness and effectiveness of the proposed methods are verified by a practical language model.
This paper investigates the issue of event-triggered adaptive finite-time state-constrained control for multi-input multi-output uncertain nonlinear systems. To prevent asymmetric time-varying state constraints from being violated, a tan-type nonlinear mapping is established to transform the considered system into an equivalent “non-constrained” system. By employing a smooth switch function in the virtual control signals, the singularity in the traditional finite-time dynamic surface control can be avoided. Fuzzy logic systems are used to compensate for the unknown functions. A suitable event-triggering rule is introduced to determine when to transmit the control laws. Through Lyapunov analysis, the closed-loop system is proved to be semi-globally practical finite-time stable, and the state constraints are never violated. Simulations are provided to evaluate the effectiveness of the proposed approach.
The conversion from constant current (CC) to constant voltage (CV) is one of the key technologies of CC underwater observatory systems. A shunt regulator with high stability and high reliability is usually used. Applications, however, are limited by high heat dissipation and low efficiency. In this paper, with an improved shunt regulation method, a novel concept of stepless power reconfiguration (SPR) for the CC/CV module is proposed. In cases with stable or slowly changing load, two modes of CC/CV conversion are proposed to reduce unnecessary power loss of the shunt regulator while being able to retain any operator-preset power margin in the system: (1) the manual SPR (MSPR) method based on single-loop control method; (2) the automatic SPR (ASPR) method based on inner-outer loop control method. The efficiency of the system is analyzed. How to select some key parameters of the system is discussed. Experimental results show that MSPR and ASPR are both effective and practical methods to reduce heat dissipation and improve the efficiency of the CC/CV module, while the high stability of the shunt regulator remains.
Physiological signal based biometric analysis has recently attracted attention as a means of meeting increasing privacy and security requirements. The real-time nature of an electrocardiogram (ECG) and the hidden nature of the information make it highly resistant to attacks. This paper focuses on three major bottlenecks of existing deep learning driven approaches: the lengthy time requirements for optimizing the hyperparameters, the slow and computationally intense identification process, and the unstable and complicated nature of ECG acquisition. We present a novel deep neural network framework for learning human identification feature representations directly from ECG time series. The proposed framework integrates deep bidirectional long short-term memory (BLSTM) and adaptive particle swarm optimization (APSO). The overall approach not only avoids the inefficient and experience-dependent search for hyperparameters, but also fully exploits the spatial information of ordinal local features and the memory characteristics of a recognition algorithm. The effectiveness of the proposed approach is thoroughly evaluated in two ECG datasets, using two protocols, simulating the influence of electrode placement and acquisition sessions in identification. Comparing four recurrent neural network structures and four classical machine learning and deep learning algorithms, we prove the superiority of the proposed algorithm in minimizing overfitting and self-learning of time series. The experimental results demonstrated an average identification rate of 97.71%, 99.41%, and 98.89% in training, validation, and test sets, respectively. Thus, this study proves that the application of APSO and LSTM techniques to biometric human identification can achieve a lower algorithm engineering effort and higher capacity for generalization.
A metasurface unit is designed operating at 2–20 GHz to enhance the gain and radiation performance of an antipodal Vivaldi antenna (AVA). The unit has a simple structure, stable ultra-wideband performance, high permittivity, and can independently modulate two polarization modes electromagnetic waves. We analyze the current distribution on the unit and extract equivalent characteristic parameters to verify the ability of independent modulation on two polarization modes electromagnetic waves. The designed metasurface unit is integrated into the aperture of the AVA and forms the metasurface lens (ML) for guiding the propagation of electromagnetic waves. Two types of ML are proposed and integrated into the AVA to design antennas Ant1 and Ant2. The modulation effect of the lens on the electromagnetic wave is analyzed from the perspective of electric field amplitude and phase, and the final design is obtained. From the optimized design results, the AVA and the proposed Ant2 are fabricated and measured, and the measurement results are in good agreement with the simulation ones. The impedance bandwidth measured by Ant2 basically covers the 2–18 GHz frequency band. Compared with the conventional AVA, the gain of the proposed Ant2 is increased by 0.6–3.7 dB, the sidelobe level is significantly reduced, and the directivity has also been clearly improved.
We present a low-power inductorless wideband differential cryogenic amplifier using a 0.13-μm SiGe BiCMOS process for a superconducting nanowire single-photon detector (SNSPD). With a shunt–shunt feedback and capacitive coupling structure, theoretical analysis and simulations were undertaken, highlighting the relationship of the amplifier gain with the tunable design parameters of the circuit. In this way, the design and optimization flexibility can be increased, and a required gain can be achieved even without an accurate cryogenic device model. To realize a flat terminal impedance over the frequency of interest, an RC shunt compensation structure was employed, improving the amplifier’s closed-loop stability and suppressing the amplifier overshoot. The S-parameters and transient performance were measured at room temperature (300 K) and cryogenic temperature (4.2 K). With good input and output matching, the measurement results showed that the amplifier achieved a 21-dB gain with a 3-dB bandwidth of 1.13 GHz at 300 K. At 4.2 K, the gain of the amplifier can be tuned from 15 to 24 dB, achieving a 3-dB bandwidth spanning from 120 kHz to 1.3 GHz and consuming only 3.1 mW. Excluding the chip pads, the amplifier chip core area was only about 0.073 mm2.
Representation of orientation is important in a six-degree-of-freedom grating interferometer but only a few studies have focused on this topic. Roll-pitch-yaw angles, widely used in aviation, navigation, and robotics, are now being brought to the field of multi-degree-of-freedom interferometric measurement. However, the roll-pitch-yaw angles are not the exact definitions the metrologists expected in interferometry, because they require a certain sequential order of rotations and may cause errors in describing complicated rotations. The errors increase as the tip and tilt angles of the grating increase. Therefore, a replacement based on fused angles in robotics is proposed and named “fused-like angles.” The fused-like angles are error-free, so they are more in line with the definitions in grating interferometry and more suitable for six-degree-of-freedom measurements. Fused-like angles have already been used in research on the kinematic model and decoupling algorithm of the six-degree-of-freedom grating interferometer.