Astrocyte-neuron communication mediated by the Notch signaling pathway:focusing on glutamate transport and synaptic plasticity

Maintaining glutamate homeostasis after hypoxic ischemia is important for synaptic function and neural cell activity,and regulation of glutamate transport between astrocyte and neuron is one of the important modalities for reducing glutamate accumulation.However,further research is needed to investigate the dynamic changes in and molecular mechanisms of glutamate transport and the effects of glutamate transport on synapses.The aim of this study was to investigate the regulatory mechanisms underlying Notch pathway mediation of glutamate transport and synaptic plasticity.In this study,Yorkshire neonatal pigs(male,age 3 days,weight 1.0–1.5 kg,n=48)were randomly divided into control(sham surgery group)and five hypoxic ischemia subgroups,according to different recovery time,which were then further subdivided into subgroups treated with dimethyl sulfoxide or a Notch pathway inhibitor(N-[N-(3,5-difluorophenacetyl-l-alanyl)]-S-phenylglycine t-butyl ester).Once the model was established,immunohistochemistry,immunofluorescence staining,and western blot analyses of Notch pathway-related proteins,synaptophysin,and glutamate transporter were performed.Moreover,synapse microstructure was observed by transmission electron microscopy.At the early stage(6–12 hours after hypoxic ischemia)of hypoxic ischemic injury,expression of glutamate transporter excitatory amino acid transporter-2 and synaptophysin was downregulated,the number of synaptic vesicles was reduced,and synaptic swelling was observed;at 12–24 hours after hypoxic ischemia,the Notch pathway was activated,excitatory amino acid transporter-2 and synaptophysin expression was increased,and the number of synaptic vesicles was slightly increased.Excitatory amino acid transporter-2 and synaptophysin expression decreased after treatment with the Notch pathway inhibitor.This suggests that glutamate transport in astrocytes-neurons after hypoxic ischemic injury is regulated by the Notch pathway and affects vesicle release and synaptic plasticity through the expression of synaptophysin.

Open World Recognition of Communication Jamming Signals

To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning capability.In this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)approach.Then,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

Signal classification method based on data mining formulti-mode radar

For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.

Modulation recognition of communication signals based on SCHKS-SSVM

A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.

Predictive Tracking Implementation for Mobile Communication Using Programmable Metasurface

The programmable metasurface(PM)is an antenna array architecture that realizes flexible beam steering.This functionality is achieved by controlling the unit cells designed with micro components such as positive-intrinsic-negative(PIN)diodes,which offers potential cost reductions in the next generation wireless communication systems.Although PM has been a popular topic in antenna design,its implementations in real-time systems accompanied by signal processing algorithms are challenging.In this paper,novel predictive tracking algorithms for mobile communication scenarios using a PM are created and implemented in a real-time system operating at 28 GHz.An angular speed prediction(ASP)algorithm is proposed to compute the position of user equipment(UE)based on the previously recorded beam directions.As another solution,an angle correction(AC)algorithm is proposed to further improve the prediction and tracking accuracy.As a benchmark,the comparisons to a previous PM tracking algorithm without prediction are presented.Both simulation and measurement results show that the prediction algorithms successfully improve the tracking performance,which also prove the feasibilities of PM-based systems to solve complex real-time signal processing problems.

BER Performance of FSO Communication System with Differential Signaling over Correlated Atmospheric Turbulence Fading

Free space optical(FSO) communication system with differential signaling possesses the advantage of requiring no channel state information and avoiding computational load or link throughput reduction compared to the systems with conventional receivers. In this work, we investigate bit error rate(BER) performance of this system over partially and fully correlated atmospheric turbulence fading. In order to conduct the above analysis, we obtain a probability density functions(PDF) of the channel fading on the differential signals and derive our instantaneous BER using differential signaling scheme. Based on these results, we develop two closed-form mathematical expressions for the average BER under fully correlated and partially correlated fading in the convergent infinite series confirmed by Cauchy’s ratio test. The accuracy of the derived BER expressions is demonstrated by the Monte Carlo simulations, and the analyses for the effects of the system parameters on the BER performance are provided.

Na^(+)/K^(+)-ATPase:ion pump,signal transducer,or cytoprotective protein,and novel biological functions

Na^(+)/K^(+)-ATPase is a transmembrane protein that has important roles in the maintenance of electrochemical gradients across cell membranes by transporting three Na^(+)out of and two K^(+)into cells.Additionally,Na^(+)/K^(+)-ATPase participates in Ca^(2+)-signaling transduction and neurotransmitter release by coordinating the ion concentration gradient across the cell membrane.Na^(+)/K^(+)-ATPase works synergistically with multiple ion channels in the cell membrane to form a dynamic network of ion homeostatic regulation and affects cellular communication by regulating chemical signals and the ion balance among different types of cells.Therefo re,it is not surprising that Na^(+)/K^(+)-ATPase dysfunction has emerged as a risk factor for a variety of neurological diseases.However,published studies have so far only elucidated the important roles of Na^(+)/K^(+)-ATPase dysfunction in disease development,and we are lacking detailed mechanisms to clarify how Na^(+)/K^(+)-ATPase affects cell function.Our recent studies revealed that membrane loss of Na^(+)/K^(+)-ATPase is a key mechanism in many neurological disorders,particularly stroke and Parkinson's disease.Stabilization of plasma membrane Na^(+)/K^(+)-ATPase with an antibody is a novel strategy to treat these diseases.For this reason,Na^(+)/K^(+)-ATPase acts not only as a simple ion pump but also as a sensor/regulator or cytoprotective protein,participating in signal transduction such as neuronal autophagy and apoptosis,and glial cell migration.Thus,the present review attempts to summarize the novel biological functions of Na^(+)/K^(+)-ATPase and Na^(+)/K^(+)-ATPase-related pathogenesis.The potential for novel strategies to treat Na^(+)/K^(+)-ATPase-related brain diseases will also be discussed.

Extraction of periodic signals in chaotic secure communication using Duffing oscillators

This paper presents a novel approach to extract the periodic signals masked by a chaotic carrier. It verifies that the driven Duffing oscillator is immune to the chaotic carrier and sensitive to certain periodic signals. A preliminary detection scenario illustrates that the frequency and amplitude of the hidden sine wave signal can be extracted from the chaotic carrier by numerical simulation. The obtained results indicate that the hidden messages in chaotic secure communication can be eavesdropped utilizing Duffing oscillators.

Analysis of reflected signal of quad rotor UAV based on model fitting in mobile communication system

In view of the many scenes of unmanned aerial vehicle(UAV)detection,a third-party signal source is used to design a receiver to monitor the UAV.It is of great significance to understand the reflection of the signal illuminating the UAV.Taking the communication base station(BS)signal as the third-party signal source,and considering the complete transmission link,reflection changes and loss fading of the communication signal,this study conducts model fitting for irregular UAV targets,simplifying complex targets into a combination of simple targets.Furthermore,the influence of the dielectric constant of the target surface and the signal irradiation angle on the signal reflection is analyzed.The analysis shows that the simulation results of this model fitting method are consistent with the results of other literature,which provides theoretical support for the detection of low and slow small targets such as UAVs.

Dual-Use Signal Design for Radar and Communication via Joint Orthogonal Signal and Phase Modulation

This paper proposes a new information modulation resorting to orthogonal signal and its phase for dual-function radar communication(DFRC)systems.Focusing on the standardized linear frequency modulation(LFM)signal by additional phase,a bank of signals enjoying satisfactory autocorrelation and cross-correlation characteristics,are generated.Then,these signals map the different information as well as their phases are also modulated to increase the communication bit rate,thus yielding a series of dual-use signals.Finally,the radar detection and communication performance of dual-use signals are also provided through numerical simulation and half-physical platform verification,confirming the effectiveness of the designed signals compared with the existing design strategy.

Sounding the alarm:Functionally referential signaling in Azure-winged Magpie

Functionally referential signals are a complex form of communication that conveys information about the external environment.Such signals have been found in a range of mammal and bird species and have helped us understand the complexities of animal communication.Corvids are well known for their extraordinary cognitive abilities,but relatively little attention has been paid to their vocal function.Here,we investigated the functionally referential signals of a cooperatively breeding corvid species,Azure-winged Magpie(Cyanopica cyanus).Through field observations,we suggest that Azure-winged Magpie uses referential alarm calls to distinguish two types of threats:’rasp’ calls for terrestrial threats and ’chatter’ calls for aerial threats.A playback experiment revealed that Azure-winged Magpies responded to the two call types with qualitatively different behaviors.They sought cover by flying into the bushes in response to the ’chatter’ calls,and flew to or stayed at higher positions in response to ’rasp’ calls,displaying a shorter response time to ’chatter’ calls.Significant differences in acoustic structure were found between the two types of calls.Given the extensive cognitive abilities of corvids and the fact that referential signals were once thought to be unique to primates,these findings are important for expanding our understanding of social communication and language evolution.

For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

Emotion Measurement Using Biometric Signal

In recent years, research on the estimation of human emotions has been active, and its application is expected in various fields. Biological reactions, such as electroencephalography (EEG) and root mean square successive difference (RMSSD), are indicators that are less influenced by individual arbitrariness. The present study used EEG and RMSSD signals to assess the emotions aroused by emotion-stimulating images in order to investigate whether various emotions are associated with characteristic biometric signal fluctuations. The participants underwent EEG and RMSSD while viewing emotionally stimulating images and answering the questionnaires. The emotions aroused by emotionally stimulating images were assessed by measuring the EEG signals and RMSSD values to determine whether different emotions are associated with characteristic biometric signal variations. Real-time emotion analysis software was used to identify the evoked emotions by describing them in the Circumplex Model of Affect based on the EEG signals and RMSSD values. Emotions other than happiness did not follow the Circumplex Model of Affect in this study. However, ventral attentional activity may have increased the RMSSD value for disgust as the β/θ value increased in right-sided brain waves. Therefore, the right-sided brain wave results are necessary when measuring disgust. Happiness can be assessed easily using the Circumplex Model of Affect for positive scene analysis. Improving the current analysis methods may facilitate the investigation of face-to-face communication in the future using biometric signals.

Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

Modulation signal classification in communication systems can be considered a pattern recognition problem.Earlier works have focused on several feature extraction approaches such as fractal feature,signal constellation reconstruction,etc.The recent advent of deep learning(DL)models makes it possible to proficiently classify the modulation signals.In this view,this study designs a chaotic oppositional satin bowerbird optimization(COSBO)with bidirectional long term memory(BiLSTM)model for modulation signal classification in communication systems.The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals.In addition,the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension(SFD)approach.Moreover,the modulation signal classification process takes place using BiLSTM with fully convolutional network(BiLSTM-FCN).Furthermore,the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO algorithm.In order to ensure the enhanced classification performance of the COSBO-BiLSTM model,a wide range of simulations were carried out.The experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.

The 11th International Conference on Wireless Communications and Signal Processing

The Annual International Conference on Wireless Communications and Signal Processing(IC-WCSP)aims to gather international researchers from academia and industry to meet and exchange ideas and recent research works across the broad field of ICT technologies.The event of WCSP 2019 will be held in Xi’an,Shaanxi,China,on October 23-25,2019.

FUNDAMENTAL COMMUNICATIONS THEORIES AND SIGNAL PROCESSING TECHNIQUES FOR AMORPHOUS CELLULAR SYSTEMS

The rapid developing of the fourth generation(4G)wireless communications has aroused tremendous demands for high speed data transmission due to the dissemination of various types of the intelligent user terminals as well as the wireless multi-media services.It is predicted that the network throughput will increase

Signal averaging for noise reduction in anesthesia monitoring and control with communication channels

This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.

Network-Connected UAV Communications: Potentials and Challenges

This article explores the use of network-connected unmanned aerial vehicle(UAV) communications as a compelling solution to achieve high-rate information transmission and support ultra-reliable UAV remote command and control. We first discuss the use cases of UAVs and the resulting communication requirements, accompanied with a flexible architecture for network-connected UAV communications. Then, the signal transmission and interference characteristics are theoretically analyzed, and subsequently we highlight the design and optimization considerations, including antenna design, nonorthogonal multiple access communications, as well as network selection and association optimization. Finally, case studies are provided to show the feasibility of network-connected UAV communications.

Fractional Fourier domain hopped communication method based on chirp modulation for underwater acoustic channels

To improve the data rate of underwater acoustic frequency-hopped communications, frequency hopping is applied to different orders of fractional Fourier domain (FrFD), to enable non-intrusive, bandwidth-limited acoustic communications. An FrFD frequency-hopped communication method based on chirp modulation, namely multiple chirp shift keying-FrFD hopping (MCSK-FrFDH), is proposed for underwater acoustic channels. Validated by both simulations and experimental results, this method can reach a bandwidth efficiency twice more than conventional frequency-hopped methods with the same data rate and anti-multipath capability, suggesting that the proposed method achieves a better performance than the traditional frequency hopped communication in underwater acoustic communication channels. Results also show that in practical scenarios, the MCSK-FrFDH system with longer symbol length performs better at the low signal-to-noise ratio (SNR), while the system with larger frequency sweeping range performs better at a high SNR.