A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs.

Body to body networks (BBNs) are a kind of large-scaled sensor network that are composed of several wireless body area networks (WBANs) in the distributed structure, and in recent decades, BBNs have played a key role in medical, aerospace, and military applications. Compared with the traditional WBANs, BBNs have larger scales and longer transmission distances. The sensors within BBNs not only transmit the data they collect, but also forward the data sent by other nodes as relay nodes.

Path Planning and Trajectory Tracking for Automatic Guided Vehicles.

Automated guided vehicle technology has become a hot area of scientific research due to its increasing use in manufacturing and logistics. Its main features are programming and control, remote computer eye tracking, command receiving and execution, autonomous route planning, and autonomous driving execution of tasks, with the advantages of high intelligence and flexibility. In this work, a simple vehicle model is used to study the route planning and tracking control of automatic guided vehicles.

A new predefined-time stability theorem and its application in the synchronization of memristive complex-valued BAM neural networks.

In this paper, two novel and general predefined-time stability lemmas are given and applied to the predefined-time synchronization problem of memristive complex-valued bidirectional associative memory neural networks (MCVBAMNNs). Firstly, different from the generally fixed-time stability lemma, the setting of an adjustable time parameter in the derived predefined-time stability lemma causes it to be more flexible and more general. Secondly, the model studied in the complex-valued BAM neural networks model, which is different from the previous discussion of the real part and imaginary part respectively.

An anonymous SIP authenticated key agreement protocol based on elliptic curve cryptography.

Designing a secure authentication scheme for session initial protocol (SIP) over internet protocol (VoIP) networks remains challenging. In this paper, we revisit the protocol of Zhang, Tang and Zhu (2015) and reveal that the protocol is vulnerable to key-compromise impersonation attacks. We then propose a SIP authenticated key agreement protocol (AKAP) using elliptic curve cryptography (ECC).

Predefined-time synchronization of competitive neural networks.

In this paper, the predefined-time synchronization of competitive neural networks (CNNs) is researched based on two different predefined-time stability theorems. In view of the bilayer structure of CNNs, we design two bilayer predefined-time controllers. The first controller utilizes sign function, while the second controller utilizes exponential function and Lyapunov function.

A Fuzzy Radial Basis Adaptive Inference Network and Its Application to Time-Varying Signal Classification.

A fuzzy radial basis adaptive inference network (FRBAIN) is proposed for multichannel time-varying signal fusion analysis and feature knowledge embedding. The model which combines the prior signal feature embedding mechanism of the radial basis kernel function with the rule-based logic inference ability of fuzzy system is composed of a multichannel time-varying signal input layer, a radial basis fuzzification layer, a rule layer, a regularization layer, and a T-S fuzzy classifier layer. The dynamic fuzzy clustering algorithm was used to divide the sample set pattern class into several subclasses with similar features.

Research on the Influence Mechanism of Epidemic Information Disclosure on Screening Authenticity Information.

In early 2020, the new coronavirus pneumonia (Referred to as COVID-19) spread rapidly throughout the country, which has a major impact on economic and social development. Timely and accurate disclosure of epidemic information will enhance the public's ability to discriminate against false information. The influence mechanism of epidemic information disclosure on the identification of true and false information was studied, and the influencing factors of epidemic information disclosure were analyzed and studied under the framework of the "Technology-Organization-Environment" (TOE) model and the two-factor theory of driving factors and impeding factors.

Invariant Image Representation Using Novel Fractional-Order Polar Harmonic Fourier Moments.

Continuous orthogonal moments, for which continuous functions are used as kernel functions, are invariant to rotation and scaling, and they have been greatly developed over the recent years. Among continuous orthogonal moments, polar harmonic Fourier moments (PHFMs) have superior performance and strong image description ability. In order to improve the performance of PHFMs in noise resistance and image reconstruction, PHFMs, which can only take integer numbers, are extended to fractional-order polar harmonic Fourier moments (FrPHFMs) in this paper.

Time Delay Complex Chen Chaotic System and Secure Communication Scheme for Wireless Body Area Networks.

Although many chaotic systems with time delays have been studied in recent years, most studies have only focused on the theoretical level, without special applications. Therefore, we present a basic introduction of a time delay complex Chen chaotic system, including the influence of parameter changes and time delay factors on the time delay system. On the basis of complex modified projection synchronization (CMPS), we detail the design of a new controller and communication scheme and apply this communication scheme to a wireless body area network (WBAN), in order to encrypt and decrypt body data collected by sensors.

Time-Delay Characteristics of Complex Lü System and Its Application in Speech Communication.

Although complex Lü systems have been considered in many studies, application of the self-time-delay synchronization (STDS) of complex Lü systems in secure speech communications does not appear to have been covered in much of the literature. Therefore, it is meaningful to study the STDS of complex Lü systems and its application in secure speech communication. First, a complex Lü system with double time-delay is introduced and its chaotic characteristics are analyzed.

Special Characteristics and Synchronizations of Multi Hybrid-Order Chaotic Systems.

Based on advantages of integer and fractional chaotic systems, hybrid chaotic systems and their definitions and some fundamental concepts are proposed, such as hybrid degree (HD), the lowest order (LO) and the total dimension order (TDO). The preliminary properties of hybrid Lorenz systems and hybrid forms of some classic chaotic systems are studied. Then, the relations between HD, LO and TDO with different parameters is investigated in chaotic systems.

Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis.

Conversational sentiment analysis is an emerging, yet challenging subtask of the sentiment analysis problem. It aims to discover the affective state and sentimental change in each person in a conversation based on their opinions. There exists a wealth of interaction information that affects speaker sentiment in conversations.

Automatic Detection of Atrial Fibrillation Based on CNN-LSTM and Shortcut Connection.

Atrial fibrillation (AF) is one of the most common persistent arrhythmias, which has a close connection to a large number of cardiovascular diseases. However, if spotted early, the diagnosis of AF can improve the effectiveness of clinical treatment and effectively prevent serious complications. In this paper, a combination of an 8-layer convolutional neural network (CNN) with a shortcut connection and 1-layer long short-term memory (LSTM), named 8CSL, was proposed for the Electrocardiogram (ECG) classification task.

Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks.

Robust cross-subject emotion recognition based on multichannel EEG has always been hard work. In this work, we hypothesize that there exist default brain variables across subjects in emotional processes. Hence, the states of the latent variables that relate to emotional processing must contribute to building robust recognition models.

Multiple Discrimination and Pairwise CNN for view-based 3D object retrieval.

With the rapid development and wide application of computer, camera device, network and hardware technology, 3D object (or model) retrieval has attracted widespread attention and it has become a hot research topic in the computer vision domain. Deep learning features already available in 3D object retrieval have been proven to be better than the retrieval performance of hand-crafted features. However, most existing networks do not take into account the impact of multi-view image selection on network training, and the use of contrastive loss alone only forcing the same-class samples to be as close as possible.

The Use of Texture Features to Extract and Analyze Useful Information from Retinal Images.

Background: The analysis of retinal images can help to detect retinal abnormalities that are caused by cardiovascular and retinal disorders.

Objective: In this paper, we propose methods based on texture features for mining and analyzing information from retinal images.

Methods: The recognition of the retinal mask region is a prerequisite for retinal image processing.

Adaptive fault-tolerant consensus for a class of leader-following systems using neural network learning strategy.

In this paper, the leader-following consensus problem of a class of nonlinearly multi-dimensional multi-agent systems with actuator faults is addressed by developing a novel neural network learning strategy. In order to achieve the desirable consensus results, a neural network learning algorithm composed of adaptive technique is proposed to on-line approximate the unknown nonlinear functions and estimate the unknown bounds of actuator faults. Then, on the basis of the approximations and estimations, a robust adaptive distributed fault-tolerant consensus control scheme is investigated so that the bounded results of all signals of the resulting closed-loop leader-following system can be achieved by using Lyapunov stability theorem.

Sparse ECG Denoising with Generalized Minimax Concave Penalty.

The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases. However, ECG signals are susceptible to noise, which may degenerate waveform and cause misdiagnosis. In this paper, the ECG noise reduction techniques based on sparse recovery are investigated.

A metamorphic testing approach for event sequences.

Test oracles are commonly used in software testing to determine the correctness of the execution results of test cases. However, the testing of many software systems faces the test oracle problem: a test oracle may not always be available, or it may be available but too expensive to apply. One such software system is a system involving abundant business processes.

An efficient dynamic ID-based remote user authentication scheme using self-certified public keys for multi-server environments.

Recently, Li et al. proposed a novel smart card and dynamic ID-based remote user authentication scheme for multi-server environments. They claimed that their scheme can resist several types of attacks.

Photoinduced Localized Hole Delays Nonradiative Electron-Hole Recombination in Cesium-Lead Halide Perovskites: A Time-Domain Ab Initio Analysis.

All-inorganic perovskites have attracted intense interest as promising photovoltaic materials due to their excellent performance. Using time domain density functional theory combined with nonadiabatic (NA) molecular dynamics, we demonstrate that a photoinduced localized polaron-like hole greatly delays the nonradiative electron-hole recombination relative to the structure with delocalized free charge of the CsPbBr. This is because localized charge carriers diminish overlap between electron and hole wave functions and decrease the NA coupling by a factor of 6.

[Design and implementation of data checking system for Chinese materia medica resources survey].

The Chinese material medica resources (CMMR) national survey information management system has collected a large amount of data. To help dealing with data recheck, reduce the work of inside, improve the recheck of survey data from provincial and county level, National Resource Center for Chinese Materia Medical has designed a data checking system for Chinese material medica resources survey based on J2EE technology, Java language, Oracle data base in accordance with the SOA framework. It includes single data check, check score, content manage, check the survey data census data with manual checking and automatic checking about census implementation plan, key research information, general survey information, cultivation of medicinal materials information, germplasm resources information the medicine information, market research information, traditional knowledge information, specimen information of this 9 aspects 20 class 175 indicators in two aspects of the quantity and quality.

[Design and implementation of mobile terminal data acquisition for Chinese materia medica resources survey].

In this paper, a data acquisition system based on mobile terminal combining GPS, offset correction, automatic speech recognition and database networking technology was designed implemented with the function of locating the latitude and elevation information fast, taking conveniently various types of Chinese herbal plant photos, photos, samples habitat photos and so on. The mobile system realizes automatic association with Chinese medicine source information, through the voice recognition function it records the information of plant characteristics and environmental characteristics, and record relevant plant specimen information. The data processing platform based on Chinese medicine resources survey data reporting client can effectively assists in indoor data processing, derives the mobile terminal data to computer terminal.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance.

Throughput assurance of wireless body area networks coexistence based on stochastic geometry.

Wireless body area networks (WBANs) are expected to influence the traditional medical model by assisting caretakers with health telemonitoring. Within WBANs, the transmit power of the nodes should be as small as possible owing to their limited energy capacity but should be sufficiently large to guarantee the quality of the signal at the receiving nodes. When multiple WBANs coexist in a small area, the communication reliability and overall throughput can be seriously affected due to resource competition and interference.