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In recent years, convolutional neural network (CNN) has achieved great success in the field of network security protection. With the popularization of smart terminals and the gradual increase of power grid informatization and digitization, the protection of power monitoring systems from various cybersecurity threads is a current scientific problem that needs to be solved urgently. To this end, this paper proposes a malware detection method based on genetic algorithm optimization of the CNN-SENet network, which firstly introduces the SENet attention mechanism into the convolutional neural network to enhance the spatial feature extraction capability of the model; then, the application programming interface (API) sequences corresponding to different software behaviors are processed by segmentation and de-duplication, which in turn leads to the sequence feature extraction through the CNN-SENet model; finally, genetic algorithm is used to optimize the hyperparameters of CNN-SENet network to reduce the computational overhead of CNN and to achieve the recognition and classification of different malware at the output layer. The examples under the public dataset containing 8 kinds of malware show that the proposed method is better than the traditional algorithmic model, and can accurately and efficiently achieve malware detection with strong generalization ability.

This paper reports a formal symbolic process virtual machine (FSPVM) denoted as FSPVM-E for verifying the reliability and security of Ethereum-based services at the source code level of smart contracts. A Coq proof assistant is employed for programming the system and for proving its correctness. The current version of FSPVM-E adopts execution-verification isomorphism, which is an application extension of Curry-Howard isomorphism, as its fundamental theoretical framework to combine symbolic execution and higher-order logic theorem proving. The four primary components of FSPVM-E include a general, extensible, and reusable formal memory framework, an extensible and universal formal intermediate programming language denoted as Lolisa, which is a large subset of the Solidity programming language using generalized algebraic datatypes, the corresponding formally verified interpreter of Lolisa, denoted as FEther, and assistant tools and libraries. The self-correctness of all components is certified in Coq. FSPVM-E supports the ERC20 token standard, and can automatically and symbolically execute Ethereum-based smart contracts, scan their standard vulnerabilities, and verify their reliability and security properties with Hoare-style logic in Coq.

This paper considers the application of non-orthogonal multiple access (NOMA) into cooperative cognitive radio (CR) networks with simultaneous wireless information and power transfer (SWIPT). For NOMA in cooperative CR networks with SWIPT, the cognitive relay harvests the transmission power from the secondary transmitter with power splitting scheme, while the fixed power allocation scheme is used for the NOMA protocol. The closed-form analytical expression of the overall outage probability for the proposed networks is derived, as well as its diversity order at high signal-to-noise ratio (SNR) region is investigated. Furthermore, compared to OMA in cooperative CR networks with SWIPT, the proposed scheme can always achieve the same diversity order, but lower overall outage performance. Compared with NOMA in cooperative CR networks using its own battery for transmission, the SWIPT NOMA in cooperative CR networks will lead to losing a little of the overall outage performance, but without losing the diversity order.

This paper investigates a cooperative uplink system where two users wish to send messages to a base station with the help of a buffer-aided relay. Transmission modes in terms of both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) are considered. For the considered system, the probability for the user-to-relay channels to successfully perform NOMA is first theoretically derived in two scenarios of dynamic and fixed power controls (PCs) at users. Then, an efficient buffer-aided hybrid NOMA/OMA based mode selection (MS) scheme is proposed, which adaptively switches between the NOMA and OMA transmission modes according to the instantaneous strength of wireless channels and the buffer state. The state transmission matrix probabilities of the corresponding Markov chain is also derived, and the performance of the proposed hybrid NOMA/OMA scheme is analyzed with closed-form expressions, in terms of sum throughput, outage probability, diversity gain, and average packet delay. For both the dynamic and fixed PCs, the proposed scheme is proved to achieve a diversity gain of two when the buffer size is not smaller than three, which means that fixed PC will not lead to a loss of diversity gain.

Recently, blockchain technology has been widely applied in the financial field. Therefore, the security of the blockchain smart contracts is among the most popular contemporary research topics. To improve the theorem-proving technology in this field, we are developing an extensible hybrid verification proof engine, denoted as FEther, for Ethereum smart contract verification. Based on Lolisa, which is a large subset of solidity mechanized in Coq, FEther guarantees the consistency between smart contracts and its formal model. Combining symbolic execution with higher order logic theorem-proving, FEther contains a set of automatic strategies that execute and verify the smart contracts in Coq with a high level of automation. Besides, in FEther, the verified code segments also can be reused to assist in the verification of other properties. The functional correctness of FEther was verified in Coq. The execution efficiency of FEther has far exceeded that of the interpreters that are developed in Coq in accordance with the standard tutorial. To the best of our knowledge, FEther is the first definitional interpreter of the solidity language in Coq.

Outdoor communication wiring is mainly used to provide a physical connection between telecommunication services. With many fibers to the X networks having been built to provide high-level reliability services and to reduce operational costs, the operator has a strong demand for an automatic optical distribution frame. Based on the mathematics of knots to study the relationship of the fiber-optic connections on the main distribution frame, we proposed the ``HIGHER ALWAYS ON TOP”method for fiber-optic switching. Two prototypes of a 1-D array of input lines and a 1-D array of output lines use a line match line and a line match circle to demonstrate these concepts. We proposed the method of constructing non-coplanar lines in a 1-D array of input lines and a 2-D array of output lines, as well as a 2-D array of input lines and a 2-D array of output lines, for scalability. We imitated the opening of a shuttle in the textile field and proposed a high-speed cross-connect method and a simulation experiment to show its feasibility.

We consider a two-user multi-relay cooperative non-orthogonal multiple access (NOMA) network with distributed space-time coding. Two dual-relay selection strategies are proposed for cooperative NOMA, namely, two-stage dual relay selection with fixed power allocation (DRS-FPA) and two-stage dual relay selection with dynamic power allocation (DRS-DPA). Furthermore, lower and upper bounds on the outage probability for the DRS-FPA scheme and the exact outage probability for the DRS-DPA scheme are obtained in closed-form, respectively. Numerical results show that the proposed two-stage DRS schemes not only yield better outage performance than the existing single relay selection schemes without sacrificing spectral efficiency, but can also achieve full diversity gain.

In recent publications, we presented a novel formal symbolic process virtual machine (FSPVM) framework that combined higher-order logic theorem proving and symbolic execution for verifying the reliability and security of smart contracts developed in the Ethereum blockchain system without suffering from standard issues surrounding reusability, consistency, and automation. A specific FSPVM, denoted as FSPVM-E, was developed in Coq based on a general, extensible, and reusable formal memory framework, an extensible and universal formal intermediate programming language, denoted as Lolisa, and a corresponding formally verified interpreter for Lolisa, denoted as FEther. However, our past work has demonstrated that the execution efficiency of the standard development of FEther is extremely low. As a result, FSPVM-E fails to achieve its expected verification effect. This paper addresses this issue by first identifying three root causes of the low execution efficiency of formal interpreters. We then build abstract models of these causes, and present respective optimization schemes for rectifying the identified conditions. Finally, we apply these optimization schemes to FEther, and demonstrate that its execution efficiency has been improved significantly.

In this paper, we first study the average sum rate of sparse code multiple access (SCMA) systems, where a general scenario is considered under the assumption that the distances between the mobile users and the base station are not necessarily identical. Closed-form analytical results are derived for the average sum rate based on which an optimal factor graph matrix is designed for maximizing the capacity of the SCMA systems. Moreover, we propose a low-complexity iterative algorithm to facilitate the design of the optimal graph matrix. Finally, Monte Carlo simulations are provided to corroborate the accuracy of the theoretical results and the efficiency of the proposed iterative algorithm.

A method of achieving integrated microspectrometer with small size, high-energy efficiency, and high spectrum resolution is proposed. This spectrometer utilizes the single-pixilated liquid crystal (LC) and a matched sparse representation algorithm. The properties of compact size and high-energy efficiency are benefit to the configuration of single-pixilated LC which forms a filter array in the time domain for the polarization interference turned by driven voltage. The high spectrum resolution is obtained with the application of sparse representation of the nonnegative L1 minimization algorithm. By theoretical derivation, the physical model of the microspectrometer is set up, and the experimental results indicate the effectiveness of spectrum reconstruction with a spectrum resolution of about 0.2 nm.

Materials that undergo an electronic phase change in a reversible manner open up new directions for research in light-matter interactions and photonic devices. The highly tunable dielectric properties, along with the spatial control of insulating and metallic domains, create photonic-crystal-like environments to control light propagation. In this brief overview, recent advances in photonics that utilize solid-state phase-change systems such as vanadium dioxide (VO2) and chalcogenides are discussed. The controllable optical properties such as the refractive index and the optical conductance between two distinct phases in these materials pave the way for switchable photonic devices with memory. Furthermore, the intermediate states with the coexistence of two phases in the vicinity of phase transition in these materials behave as a tunable metamaterial with extraordinary optical properties that is promising for applications such as perfect absorbers.

Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination (${R}^{{2}}\text {)}$ and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario. [ABSTRACT FROM AUTHOR]

The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the variation of MUAP profiles caused by multiple factors in realistic conditions challenges the accurate classification of MUAPs. The goal of this study was to propose an effective method based on the convolutional neural network (CNN) to classify MUAPs with high levels of variation for MU tracking. MUAP variation was added artificially in the synthetic electromyogram (EMG) signals and was induced by changing the forearm postures in the experimental study. The proposed overlapped-segment-wise EMG decomposition method and the spike-triggered averaging method were combined to obtain the MUAP waveform samples of individual MUs in the experimental study, and the MUAP profile classification performance was tested. Since the ground-truth of MU discharge activities was known for the synthetic EMG, the MU tracking performance was further verified by mimicking the tracking procedure of MU discharge activities and the spike consistency with the true spike trains was tested in the simulation study. The conventional MUAP similarity index (SI)-based method was also performed as comparison. For both the experimental and the synthetic EMG signals, the CNN-based method significantly improved the MUAP tracking performance compared with the conventional SI-based method manifested as a higher classification accuracy (93.3%±5.4% vs 56.2%±13.9%) in the experimental study or higher spike consistency (71.1%±10.2% vs 29.2%±11.0%) in the simulation study with a smaller variation. These results demonstrated the efficiency and robustness of the proposed method to distinguish MUAPs with large variations accurately. Further development of the proposed method can promote the study on the physiological and pathological changes of the neuromuscular system where tracking MU activities is needed. [ABSTRACT FROM AUTHOR]