A Support Data-Based Core-Set Selection Method for Signal Recognition

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.

Multi-channel neural signal stimulating module and in-vivo experiments

The module for function electrical stimulation （FES） of neurons is designed for the research of the neural function regeneration microelectronic system, which is an in-body embedded micro module. It is implemented by using discrete devices at first and characterized in vitro. The module is used to stimulate sciatic nerve and spinal cord of rats and rabbits for in-vivo real-time experiments of the neural function regeneration system. Based on the module, a four channel module for the FES of neurons is designed for 12 sites cuff electrode or 10 sites shaft electrode. Three animal experiments with total five rats and two rabbits were made. In the in-vivo experiment, the neural signals including spontaneous and imitated were regenerated by the module. The stimulating signal was used to drive sciatic nerve and spinal cord of rats and rabbits, successfully caused them twitch in different parts of their bodies, such as legs, tails, and fingers. This testifies that the neural function regeneration system can regenerate the neural signals.

Measuring Absolute Delay of RNSS Signal Channel Using Digital Envelope Detection

The absolute delay caused by equipment of radio navigation satellite service (RNSS) signal channel must be calibrated in the application of positioning. The measurement accuracy of absolute delay will affect the positioning precision of RNSS user. An absolute delay measurement technique using digital envelope detection was developed for RNSS signal transmission channel. The RNSS transmission signal of navigation satellite and the one pulse per second (1PPS)generated by satellite time keeping system were sampled synchronously. With sampling data of 1PPS,the reference point of the absolute delay can be decided at first,and then sampling data of RNSS transmission signal were truncated. The truncated data were processed using digital envelop detection algorithm to search the phase converting points of RNSS signal. Finally,the absolute delay of RNSS signal transmitting channel was calculated. Uncertainty of measurement with proposed technique is lower than 0. 2 ns as the sampling frequency is 10 GHz.

Single channel signal component separation using Bayesian estimation

A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.

Single channel source separation of radar fuze mixed signal based on phase difference analysis

A new method based on phase difference analysis is proposed for the single-channel mixed signal separation of single-channel radar fuze.This method is used to estimate the mixing coefficients of de-noised signals through the cumulants of mixed signals,solve the candidate data set by the mixing coefficients and signal analytical form,and resolve the problem of vector ambiguity by analyzing the phase differences.The signal separation is realized by exchanging data of the solutions.The waveform similarity coefficients are calculated,and the time鈥攆requency distributions of separated signals are analyzed.The results show that the proposed method is effective.

Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

Reduction of signal reflection along through silicon via channel in high-speed three-dimensional integration circuit

The through silicon via （TSV） technology has proven to be the critical enabler to realize a three-dimensional （3D） gigscale system with higher performance but shorter interconnect length. However, the received digital signal after trans- mission through a TSV channel, composed of redistribution layers （RDLs）, TSVs, and bumps, is degraded at a high data-rate due to the non-idealities of the channel. We propose the Chebyshev multisection transformers to reduce the signal reflec- tion of TSV channel when operating frequency goes up to 20 GHz, by which signal reflection coefficient （$11） and signal transmission coefficient （$21） are improved remarkably by 150% and 73.3%, respectively. Both the time delay and power dissipation are also reduced by 4% and 13.3%, respectively. The resistance-inductance-conductance-capacitance （RLGC） elements of the TSV channel are iterated from scattering （S）-parameters, and the proposed method of weakening the signal reflection is verified using high frequency simulator structure （HFSS） simulation software by Ansoft.

Adaptive controller design based on input-output signal selection for voltage source converter high voltage direct current systems to improve power system stability

An input-output signal selection based on Phillips-Heffron model of a parallel high voltage alternative current/high voltage direct current(HVAC/HVDC) power system is presented to study power system stability. It is well known that appropriate coupling of inputs-outputs signals in the multivariable HVDC-HVAC system can improve the performance of designed supplemetary controller. In this work, different analysis techniques are used to measure controllability and observability of electromechanical oscillation mode. Also inputs–outputs interactions are considered and suggestions are drawn to select the best signal pair through the system inputs-outputs. In addition, a supplementary online adaptive controller for nonlinear HVDC to damp low frequency oscillations in a weakly connected system is proposed. The results obtained using MATLAB software show that the best output-input for damping controller design is rotor speed deviation as out put and phase angle of rectifier as in put. Also response of system equipped with adaptive damping controller based on HVDC system has appropriate performance when it is faced with faults and disturbance.

Signal Detection and Channel Estimation in OTFS

Orthogonal time frequency space(OTFS)modulation is a recently proposed modulation scheme that exhibits robust performance in high-Doppler environments.It is a two-dimensional modulation scheme where information symbols are multiplexed in the de⁃lay-Doppler(DD)domain.Also,the channel is viewed in the DD domain where the chan⁃nel response is sparse and time-invariant for a long time.This simplifies channel estima⁃tion in the DD domain.This paper presents an overview of the state-of-the-art approaches in OTFS signal detection and DD channel estimation.We classify the signal detection ap⁃proaches into three categories,namely,low-complexity linear detection,approximate max⁃imum a posteriori(MAP)detection,and deep neural network(DNN)based detection.Simi⁃larly,we classify the DD channel estimation approaches into three categories,namely,separate pilot approach,embedded pilot approach,and superimposed pilot approach.We compile and present an overview of some of the key algorithms under these categories and illustrate their performance and complexity attributes.

Optimization of MQAM Modulation Schemes in Mobile Communications(Ⅰ)——Analysis of MQAM BER Performance in AGWN Channel Based on Signal Space

Based on signal space concepts, a transmission error code performance for MPSK, square MQAM and star MQAM modulation schemes on the AGWN channel is analyzed. The corresponding BER formulas and computer aided numeric results are also given. Therefore it provides a theoretical basis for choosing MQAM modulation schemes in mobile communications.

Study of ultra-wideband radar signals-generated technology using two-channel signal generator

Synthesis of ultra-wideband （UWB） linear frequency modulation radar signals is a very important technology for microwave imaging, target identification and detection of low radar-cross-section （RCS） targets. A new method of UWB radar signals generation with two-channel signal generator is presented. The realization structure is given; the principle and errors of signal synthesis are analyzed. At the same time, an automatic .adjustment measure of signal phase is proposed because of phase discontinuity of waveform in this method. The simulation experiment and analysis results indicate that radar signals with large instantaneous bandwidth can be generated by means of this method on the condition that the high-speed digital devices are limited.

Selection of optimal window length using STFT for quantitative SNR analysis of LFM signal

An adaptive approach to select analysis window param- eters for linear frequency modulated （LFM） signals is proposed to obtain the optimal 3 dB signal-to-noise ratio （SNR） in the short- time Fourier transform （STFT） domain. After analyzing the instan- taneous frequency and instantaneous bandwidth to deduce the relation between the window length and deviation of the Gaus- sian window, high-order statistics is used to select the appropriate window length for STFT and get the optimal SNR with the right time-frequency resolution according to the signal characteristic under a fixed sampling rate. Computer simulations have verified the effectiveness of the new method.

Read channel for signal waveform modulation multi-level disc

A novel read channel for signal waveform modulation multi-level disc is presented in this paper. This read channel employs timing recovery system and partial response maximum likelihood detector. Compared to the previous read channel composed of level detection and run-length detection, the present read channel shows superiority in capacity increase and robust performance. Especially, relying on the partial response maximum likelihood detection, lower bit error rate can be obtained.

Transfer characteristics of dynamic biochemical signals in non-reversing pulsatile flows in a shallow Y-shaped microfluidic channel: signal filtering and nonlinear amplitude-frequency modulation

The transports of the dynamic biochemical signals in the non-reversing pulsatile flows in the mixing microchannel of a Y-shaped microfluidic device are ana- lyzed. The results show that the mixing micro-channel acts as a low-pass filter, and the biochemical signals are nonlinearly modulated by the pulsatile flows, which depend on the biochemical signal frequency, the flow signal frequency, and the biochemical signal transporting distance. It is concluded that, the transfer characteristics of the dynamic biochemical signals, which are transported in the time-varying flows, should be carefully considered for better loading biochemical signals on the cells cultured on the bottom of the microfluidic channel.

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.

5G Oriented Exponential Effective Signal to Interference plus Noise Ratio Mapping Algorithm Based on Channel Classification

In the future the fifth generation( 5 G) communication systems,channel models may be very complicated and it is difficult to calculate equivalent signal to interference plus noise ratio( SINR)of a random fading channel. Therefore,methods for the calculation of equivalent SINR of a random fading channel are very necessary.In this paper,an enhanced algorithm on the exponential effective SINR mapping( EESM) model for random fading channels was proposed. First, the optimal adjustment parameters of typical channel fading models including extended pedestrian A( EPA)model,extended vehicular A( EVA) model and extended typical urban( ETU) model were obtained by simulation. Then the proposed solution was used to actualize channel classification according to the maximum multipath delay and the average power of the random channel. The solution can determine the typical channel closest to random channel for obtaining the optimal adjustment value of EESM. The evaluation results indicate that the proposed one can improve the whole system throughput significantly and meanwhile the accuracy of the link prediction algorithm is also guaranteed.

Fragmental Weight-Conservation Combining Scheme for Statistical Signal Transmissions under Fast Time-Varying Channels

Statistical Signal Transmission(SST)is a technique based on orthogonal frequency-division multiplexing(OFDM)and adopts cyclostationary features,which can transmit extra information without additional bandwidth.However,the more complicated environment in 5G communication systems,especially the fast time-varying scenarios,will dramatically degrade the performance of the SST.In this paper,we propose a fragmental weight-conservation combining(FWCC)scheme for SST,to overcome its performance degradation under fast time-varying channels.The proposed FWCC scheme consists of three phases:1、incise the received OFDM stream into pieces;2、endue different weights for fine and contaminated pieces,respectively;3、combine cyclic autocorrelation function energies of all the pieces;and 4、compute the final feature and demodulate data of SST.Through these procedures above,the detection accuracy of SST will be theoretically refined under fast time-varying channels.Such an inference is confirmed through numerical results in this paper.It is demonstrated that the BER performance of proposed scheme outperforms that of the original scheme both in ideal channel estimation conditions and in imperfect channel estimation conditions.In addition,we also find the experiential optimal weight distribution strategy for the proposed FWCC scheme,which facilitates practical applications.

Region-Aware Trace Signal Selection Using Machine Learning Technique for Silicon Validation and Debug

In today’s modern design technology,post-silicon validation is an expensive and composite task.The major challenge involved in this method is that it has limited observability and controllability of internal signals.There will be an issue during execution how to address the useful set of signals and store it in the on-chip trace buffer.The existing approaches are restricted to particular debug set-up where all the components have equivalent prominence at all the time.Practically,the verification engineers will emphasis only on useful functional regions or components.Due to some constraints like clock gating,some of the regions can be ignored during execution.Likewise,some of these regions can be verified deeply and have minimum errors compared to other control regions.The proposed system focusses on random signals that identify more errors which are prone to signal selection technique with low area overhead.To enhance the observability,a machine learning technique is developed.Based on the training samples of smaller designs,a model is developed to find out the contiguous neighbours of each flip-flop.This can eliminate the obstacles of unknown signals.This system demonstrates using Opencores and ISCAS’89 benchmark circuits that result in easy and fast error detection compared to the state-of-theart of other methods.This is also verified using gate-level error models by cross-validation of each debug run.

Simulation of the physical process of neural electromagnetic signal generation based on a simple but functional bionic Na^(+)channel

The physical processes occurring at open Na^(+) channels in neural fibers are essential for the understanding of the nature of neural signals and the mechanism by which the signals are generated and transmitted along nerves.However,there is a less generally accepted description of these physical processes.We studied changes in the transmembrane ionic flux and the resulting two types of electromagnetic signals by simulating the Na^(+) transport across a bionic nanochannel model simplified from voltage-gated Na^(+) channels.The results show that the Na^(+) flux can reach a steady state in approximately 10 ns due to the dynamic equilibrium of the Na^(+) ion concentration difference between both sides of the membrane.After characterizing the spectrum and transmission of these two electromagnetic signals,the low-frequency transmembrane electric field is regarded as the physical quantity transmitting in the waveguide-like lipid dielectric layer and triggering the neighboring voltage-gated channels.Factors influencing the Na^(+) flux transport are also studied.The impact of the Na^(+) concentration gradient is found to be higher than that of the initial transmembrane potential on the Na^(+) transport rate,and introducing the surface-negative charge in the upper third channel could increase the transmembrane Na^(+) current.This work can be further studied by improving the simulation model;however,the current work helps to better understand the electrical functions of voltage-gated ion channels in neural systems.

Keystone Symposia: Transmembrane Signaling by GPCRs and Channels

Description Cellular membranes present natural borders for signal transduction between cells and their environment. Nature developed different strategies to enable signals to cross the membrane barrier. The goal of this meeting is to discuss the molecular mechanisms of transmembrane signaling on the basis of three protein classes, i.e.