Transmission Signal Research Articles

Modulation instability, bifurcation analysis and solitonic waves in nonlinear optical media with odd-order dispersion

In this paper, modulation instability, bifurcation analysis, and soliton solutions are investigated in nonlinear media with odd-order dispersion terms. A generalized nonlinear Schrödinger equation with fourth-order dispersion and cubic-quintic nonlinearity is considered. A linear analysis method is used to derive an expression of the modulation instability spectrum, and the effects of the fourth-order and group velocity dispersion are pointed out on the modulation instability bands. The results show that for negative values of the fourth order in a normal dispersion regime, the modulation instability vanishes. Another important aspect is revealed when the fourth-order dispersion gets positive and has high values; additional bands emerge to enlarge the bandwidths of the modulation instability. To further confirm the effects of the odd-order dispersion in the nonlinear structure, the bifurcation, phase portraits, and chaotic behaviors have been investigated to show how instability arises in the nonlinear structure. Using numerical simulation, in particular the Runge-Kutta algorithm, the sensitivity of nonlinear systems is pointed out to confirm an unstable behavior. This investigation confirms once again the fact that the modulation spectrum is sensitive to higher-order dispersion in normal and anomalous dispersion regimes. A traveling wave hypothesis is employed to lead to the direct integration of the nonlinear system, and some specific soliton solutions are extracted. For particular constraint conditions on the discriminant, bright and dark solitons as well as Jacobi elliptic function solutions emerge. The obtained results could be used to improve the transmission signal via the optical fiber and secure the data in communication systems.

Protein Sequencing with Artificial Intelligence: Machine Learning Integrated Phosphorene Nanoslit.

Achieving high throughput protein sequencing at single molecule resolution remains a daunting challenge. Herein, relying on a solid-state 2D phosphorene nanoslit device, we report an extraordinary biosensor to rapidly identify the key signatures of all twenty amino acids using interpretable machine learning (ML) model. The XGBoost regression algorithm allows the determination of transmission function of all twenty amino acids with high accuracy. The resultant ML and DFT studies reveal that it is possible to identify individual amino acids through transmission and current signals readouts with high sensitivity and selectivity. Moreover, we thoroughly compared our results to those from graphene nanoslit and found that the phosphorene nanoslit device can be an ideal candidate for protein sequencing up to a 13-fold increase in transmission sensitivity. The present study facilitates high throughput screening of all twenty amino acids and can be further extended to other biomolecules for disease diagnosis and therapeutic decision-making.

Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks

In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Such obstacles lead to multipath effects, which could substantially impact data transmission due to fading. Therefore, quantifying the impact of buildings on transmission quality is a key parameter of the propagation model, especially in critical scenarios involving emergency vehicles where reliable communication is of utmost importance. In this research, we propose a supervised learning approach based on Artificial Neural Networks (ANNs) to develop a predictive model capable of estimating the level of signal degradation, represented by the Bit Error Rate (BER), based on the obstacles perceived by moving emergency vehicles. By establishing a relationship between the level of signal degradation and the encountered obstacles, our proposed mechanism enables efficient routing decisions being made prior to the transmission process. Consequently, data packets are routed through paths that exhibit the lowest BER. To collect the training data, we employed Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban MObility (SUMO) simulator, leveraging real-world data sourced from the OpenStreetMap (OSM) geographic database. OSM enabled us to gather geospatial data related to the Two-Dimensional (2D) geometric structure of buildings, which served as input for our Artificial Neural Network (ANN). To determine the most suitable algorithm for our ANN, we assessed the accuracy of ten learning algorithms in MATLAB, utilizing five key metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R), and Maximum Prediction Error (MaxPE). For each algorithm, we conducted fifteen iterations based on ten hidden neurons and gauged its accuracy against the aforementioned metrics. Our analysis highlighted that the ANN underpinned by the Conjugate Gradient With Powell/Beale Restarts (CGB) learning algorithm exhibited superior performance in terms of MSE, RMSE, MAE, R, and MaxPE compared to other algorithms such as Levenberg–Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Fletcher–Powell Conjugate Gradient (CGF), Polak–Ribiére Conjugate Gradient (CGP), One-Step Secant (OSS), and Variable Learning Rate Backpropagation (GDX). The BER prediction by our ANN incorporates the TWO-RAY Ground (TRG) propagation model, an adjustable parameter within NS-3. When subjected to 300 new samples, the trained ANN’s simulation outcomes illustrated its capability to learn, generalize, and successfully predict the BER for a new data instance. Overall, our research contributes to enhancing the performance and reliability of communication in urban VANET environments, especially in critical scenarios involving emergency vehicles, by leveraging supervised learning and artificial neural networks to predict signal degradation levels and optimize routing decisions accordingly.

DNA-based programmable gate arrays for general-purpose DNA computing.

The past decades have witnessed the evolution of electronic and photonic integrated circuits, from application specific to programmable1,2. Although liquid-phase DNA circuitry holds the potential for massive parallelism in the encoding and execution of algorithms3,4, the development of general-purpose DNA integrated circuits (DICs) has yet to be explored. Here we demonstrate a DIC system by integration of multilayer DNA-based programmable gate arrays (DPGAs). We find that the use of generic single-stranded oligonucleotides as a uniform transmission signal can reliably integrate large-scale DICs with minimal leakage and high fidelity for general-purpose computing. Reconfiguration of a single DPGA with 24 addressable dual-rail gates can be programmed with wiring instructions to implement over 100 billion distinct circuits. Furthermore, to control the intrinsically random collision of molecules, we designed DNA origami registers to provide the directionality for asynchronous execution of cascaded DPGAs. We exemplify this by a quadratic equation-solving DIC assembled with three layers of cascade DPGAs comprising 30 logic gates with around 500 DNA strands. We further show that integration of a DPGA with an analog-to-digital converter can classify disease-related microRNAs. The ability to integrate large-scale DPGA networks without apparent signal attenuation marks a key step towards general-purpose DNA computing.

A Space–Time–Range Joint Adaptive Focusing and Detection Method for Multiple Input Multiple Output Radar

The Multiple Input Multiple Output (MIMO) radar, as a new type of radar, emits orthogonal waveforms, which provide it with waveform diversity characteristics, leading to increased degrees of freedom and improved target detection performance. However, it also poses challenges such as difficulty in meeting higher data demand, separating waveforms, and suppressing the multidimensional sidelobes (range sidelobes, Doppler sidelobes, and angle sidelobes) of targets. Phase-coded signals are frequently employed as orthogonal transmission signals in the MIMO radar. However, these signals exhibit poor Doppler sensitivity, and the intra-pulse Doppler frequency shift can have an impact on the effectiveness of the matching filtering process. To address the aforementioned concerns, this paper presents a novel approach called the Space–Time–Range Joint Adaptive Focusing and Detection (STRJAFD) method. The proposed method utilizes the Mean Square Error (MSE) criterion and integrates spatial, temporal, and waveform dimensions to achieve efficient adaptive focusing and detection of targets. The experimental results demonstrate that the proposed method outperforms conventional cascaded adaptive methods in effectively addressing the matching mismatch issue caused by Doppler frequency shift, achieving super-resolution focusing, possessing better suppression effects on three-dimensional sidelobes and clutter, and exhibiting better detection performance in low signal-to-clutter ratio and low signal-to-noise ratio environments. Furthermore, STRJAFD is unaffected by coherent sources and demands less data.

Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network

This study's goal is to provide an early analysis of deep learning (DL) for signal identification in wireless systems that use non-orthogonal multiple access (NOMA). The successive interference cancellation (SIC) approach is frequently used at the receiver in NOMA systems when several users are decoded successively. Without explicitly calculating channels, a DL-based NOMA receiver can decode messages for several users at once. To estimate the multiuser uplink channel (CE) and recognize the initial broadcast signal in this study, it is recommended that a deep neural network with bi-directional long short-term memory (Bi-LSTM) be utilized. The suggested Bi-LSTM model, in contrast to conventional CE techniques, may immediately retrieve transmission signals impacted by channel distortion. During the offline training phase, the Bi-LTSM model is trained using simulation data based on channel statistics. The trained model is then applied to retrieve the transmitted symbols in the stage of online deployment. According to the findings, the DL method could outperform a maximum probability detector that considers interference effects when inter-symbol interference is substantial.

Minus-C odorant binding protein TcasOBP7G contributes to reproduction and defense against phytochemical in the red flour beetle, Tribolium castaneum

Insect odorant binding proteins (OBPs) paly an important role in the recognition and transmission signals. One odorant binding protein gene, TcasOBP7G, was significantly up-regulated after exposure to eucalyptol. Nevertheless, the causal role of TcasOBP7G in Tribolium castaneum is still unclear. Here, TcasOBP7G was cloned and characterized from T. castaneum, aligning with the typical characteristic of Minus-C OBPs. The analysis of gene expression profile showed that TcasOBP7G is highly expressed at late larval and adult stage, with particularly high expression levels in ovary and testis of adults, suggesting that TcasOBP7G might be associated with reproduction of T. castaneum. Furthermore, RNAi of TcasOBP7G led to a decrease in spawning, while the development of gonads was normal, suggesting that TcasOBP7G may medicate the reproduction of T. castaneum. Additionally, the mRNA level of TcasOBP7G was significantly increased after exposure to eucalyptol, and knockdown of TcasOBP7G resulted in an increased susceptibility of T. castaneum to eucalyptol, indicating that TcasOBP7G is associated with the tolerance of T. castaneum to eucalyptol. Altogether, these results indicate that TcasOBP7G is required for both reproduction and defense against phytochemical in T. castaneum. This study will provide a theoretical basis for the development of novel control strategies based on the olfaction of insects.

Analysis on the change characteristics of waveform during the transmission of continuous wave mud pulse signal

Continuous wave mud pulse transmission is the most widely used information transmission technology in measurement while drilling (MWD). The increase in transmission distance will cause the signal attenuation and deformation during transmission, the affecting exploration efficiency. The waveform change law of continuous mud pulse wave in the process of downhole transmission, this paper establishes a mathematical analysis model, focuses on the modulation form of continuous wave signal, physical mud characteristics, and drill pipe characteristics, and discusses the transmission rules of various characteristics on continuous pressure wave. A simulation system of drill pipe transmission pressure wave is constructed. The transmission characteristics of continuous wave signals under different modulation forms are discussed. The positive correlation between the amplitude attenuation and waveform distortion of pulse signals caused by increased gas holdup in the mud under bubbly flow patterns is analyzed. The numerical calculation and simulation analysis results show that the viscosity frequency characteristic of mud is the main factor affecting the attenuation of continuous wave signal. The surface roughness, size and other physical characteristics of drill pipe can affect the transmission speed and attenuation of signals in long-distance transmission. The signal is prone to distortion at the position of modulation phase change.

Application of Incoherent Multi-Frequency Signals for Information Transmission in a Nonstationary Hydroacoustic Environment

A class of digital incoherent methods of information transmission based on multi-frequency signals for mobile underwater complexes is presented, which does not require regular and accurate estimation of channel parameters and is a priori resistant to various types of interference in nonstationary hydroacoustic communication channels, and is oriented to solving communication and navigation problems for underwater robotic complexes. The limitation of spectral efficiency of such multi-frequency systems of information transmission by the value of 0.5 bits/s/Hz in various frequency ranges, nonstationary hydrological conditions, with achievement of the maximum range at acceptable levels of error probability during information decoding is shown. The operability of the proposed class of multi-frequency multiplexing methods was confirmed by numerical and in situ sea experiments on the shelf at distances from 2.5 to 7 km with the mutual drifting of ships and with sea waves of up to 3 points.

Temperature distribution during welding measured by neutron imaging

This study was carried out to investigate the neutron transmission signal as a function of sample temperature during a welding process. A theoretical description that includes the Debye-Waller factor was used to describe the temperature influence on the neutron cross-sections. Neutron imaging using a monochromatic beam helps to observe transmission variations related to the material temperature. In-situ neutron imaging of welding experiments show the distribution of the temperature in bulk steel samples. The performed finite element modelling of expected temperature distributions shows good agreement with the obtained experimental data.

Effective Excess Noise Suppression in Continuous-Variable Quantum Key Distribution through Carrier Frequency Switching.

Continuous-variable quantum key distribution (CV-QKD) is a promising protocol that can be easily integrated with classical optical communication systems. However, in the case of quantum-classical co-transmissions, such as dense wavelength division multiplexing with classical channels and time division multiplexing with large-power classical signal, a quantum signal is more susceptible to crosstalk caused by a classical signal, leading to signal distortion and key distribution performance reduction. To address this issue, we propose a noise-suppression scheme based on carrier frequency switching (CFS) that can effectively mitigate the influence of large-power random noise on the weak coherent state. In this noise-suppression scheme, a minimum-value window of the channel's noise power spectrum is searched for and the transmission signal frequency spectrum shifts to the corresponding frequency to avoid large-power channel noise. A digital filter is also utilized to filter out most of the channel noise. Simulation results show that compared to the traditional fixed carrier frequency scheme, the proposed noise-suppression scheme can reduce the excess noise to 1.8%, and the secret key rate can be increased by 1.43 to 2.86 times at different distances. This noise-suppression scheme is expected to be applied in scenarios like quantum-classical co-transmission and multi-QKD co-transmission to provide noise-suppression solutions.

Design of Self-Integrating Transient Surface Current Density Sensor Integrated Fiber Transmission Link.

The transient surface current density reflects the external coupling of the electromagnetic pulse (EMP) to the tested device. In this paper, the generation mechanism and measurement principle of conductor surface current density are introduced, and the surface current density distribution irradiated by EMP on a typical aircraft structure is simulated and analyzed. The traditional surface current density is usually measured by B-dot antenna, but its output signal is the differential of the measured signal, so additional integrators or numerical integration of the measured data are required. In this paper, a self-integrating surface current sensor based on optical fiber transmission is designed based on the shielded loop antenna with gap structure. The output signal is the real signal waveform to be measured. Compared with coaxial cables, integrated optical fiber transmission improves the anti-interference ability of long-distance transmission signals. At the same time, the design process of the sensor is introduced in detail. The bandwidth of the sensor is 300 kHz~500 MHz, the sensitivity is calibrated at 1.23 (A·m-1)/mV, and the dynamic range is ±25~1400 A·m-1 (35 dB). The surface current of a metal plate is measured in a bounded wave electromagnetic pulse simulator using a detector developed in this paper. The test results show that the developed sensor has good engineering applicability.

Light wave propagation and diffraction inhibition in three-dimensional photonic quasicrystal lattices

We have investigated the linear propagation of light wave in three-dimensional photonic quasicrystal lattices by numerical simulation. The diffraction characteristics of the light waves in the longitudinal non-modulated, in-phase modulated and out-of-phase modulated quasicrystal lattices have been compared and discussed. We have demonstrated the diffraction inhibition of light wave in three-dimensional photonic quasicrystals. Linear transverse light localization has been realized in the longitudinal out-of-phase modulated three-dimensional quasicrystal lattices by adjusting the structural parameters appropriately. Furthermore, the results also show that the longitudinal out-of-phase modulated photonic quasicrystals are easier to produce diffraction inhibition than the longitudinal out-of-phase modulated periodic lattices under low transverse refractive index contrast. Distortionless dot image transmission has numerically demonstrated in out-of-phase modulated photonic quasicrystals array. It provides a new way for distortion-free image transmission, which has promising applications in optical information transmission and all-optical signal processing.

Sustainable Traffic Regulation System in Protected Areas: Pilot Technology Testing in National Park in the Czech Republic

In the context of nature protection, there is an effort to regulate individual car traffic in protected areas. In the framework of the research, a pilot testing of a vehicle detection and identification system in the Krkonoše National Park was carried out using two selected technologies (license plate recognition and Bluetooth token detection). The research was carried out under conditions of poorer availability of mobile signal for transmission of measured data, lack of electrical power supply, and in challenging climatic conditions in the mountains. The main objective was to verify the applicability and limits of the mentioned technologies under these difficult conditions. For this purpose, two test sites were built: a fixed and a mobile point. Testing at both points was carried out using two basic methods, namely online through continuous data collection from the detectors and on-site through a local survey during the summer of 2022. The parameters evaluated were the reliability of the vehicle identification itself and the reliability of the operation of the individual detection subsystems and the tested system as a whole. The results show that the license plate recognition system using two cameras for the checkpoint shows a high recognition reliability, but it is reduced for some types of vehicles (especially motorcycles and four-wheelers). At the same time, this technology is demanding on energy resources. Detection using a Bluetooth scanner has proven to be highly reliable up to 50 km/h. A reliable power supply is necessary to achieve high reliability, which was a problem at the mobile point. Evaluation of images from cameras with motion detection showed the limits of this technology, which increased with increasing vehicle speed. The system can be used to detect traffic in protected areas, taking into account the limits specified in this article.

Modified dipole-dipole interactions in the presence of a nanophotonic waveguide

When an emitter ensemble interacts with the electromagnetic field, dipole-dipole interactions are induced between the emitters. The magnitude and shape of these interactions are fully determined by the specific form of the electromagnetic field modes. If the emitters are placed in the vicinity of a nanophotonic waveguide, such as a cylindrical nanofiber, the complex functional form of these modes makes the analytical evaluation of the dipole-dipole interaction cumbersome and numerically costly. In this work, we provide a full detailed description of how to successfully calculate these interactions, outlining a method that can be easily extended to other environments and boundary conditions. Such exact evaluation is of importance as, due to the collective character of the interactions and dissipation in this kind of systems, any small modification of the interactions may lead to dramatic changes in experimental observables, particularly as the number of emitters increases. We illustrate this by calculating the transmission signal of the light guided by a cylindrical nanofiber in the presence of a nearby chain of emitters.

Radio frequency based distributed system for noncooperative UAV classification and positioning

With the increasing popularity of civilian unmanned aerial vehicles (UAVs), safety issues arising from unsafe operations and terrorist activities have received growing attention. To address this problem, an accurate classification and positioning system is needed. Considering that UAVs usually use radio frequency (RF) signals for video transmission, in this paper, we design a passive distributed monitoring system that can classify and locate UAVs according to their RF signals. Specifically, three passive receivers are arranged in different locations to receive RF signals. Due to the noncooperation between a UAV and receivers, it is necessary to detect whether there is a UAV signal from the received signals. Hence, convolutional neural network (CNN) is proposed to not only detect the presence of the UAV, but also classify its type. After the UAV signal is detected, the time difference of arrival (TDOA) of the UAV signal arriving at the receiver is estimated by the cross-correlation method to obtain the corresponding distance difference. Finally, the Chan algorithm is used to calculate the location of the UAV. We deploy a distributed system constructed by three software defined radio (SDR) receivers on the campus playground, and conduct extensive experiments in a real wireless environment. The experimental results have successfully validated the proposed system.

Retracted: Mathematical Methods on Optimal Frequency Selection Model for Wireless Communication Transmission Signal under 5G Internet of Everything

Retracted: Mathematical Methods on Optimal Frequency Selection Model for Wireless Communication Transmission Signal under 5G Internet of Everything

Microwave photonic dispersion immune self-interference cancellation scheme for a radio over fiber based in-band full duplex communication link

A microwave photonic scheme for multi-functional radio over fiber based in-band full duplex communication links to realize deep self-interference cancellation, long range transmission, and efficient signal recovery is proposed. In the proposed scheme, double sideband modulation helps avoid applying electrical couplers or optical filters and is beneficial to improve the frequency conversion efficiency. By virtue of the intermediate frequency signal amplitude regulation mechanism induced by fiber dispersion, the conditions of dispersion induced power fading compensation, amplitude matching, and phase reversion between self-interference and reference signals can be satisfied through joint DC bias tuning. The high precision delay matching in the optical domain is advantageous to the deep self-interference cancellation performance in a wide frequency range compared to its electrical counterparts. Furthermore, the deterioration of signal quality and self-interference cancellation performance caused by fiber dispersion is avoided. Experimental analyses show that a single tone signal in a wide frequency range of 2–20 GHz can be cancelled over 60 dB. A broadband signal with bandwidth of 50 MHz in the C-band, X-band, and K-band can be cancelled over 35 dB. Furthermore, the recovery performance of the signal of interest with different modulation formats and different signal to interference ratios is investigated, showing high quality signal transmission and efficient signal recovery. The capability of dispersion induced power fading compensation is also verified, and the spurious-free dynamic range is measured to be 88.47dB⋅Hz2/3.

Deep Convolutional and Recurrent Neural-Network-Based Optimal Decoding for RIS-Assisted MIMO Communication

The reconfigurable intelligent surface (RIS) is one of the most innovative and revolutionary technologies for increasing the effectiveness of wireless systems. Deep learning (DL) is a promising method that can enhance system efficacy using powerful tools in RIS-based environments. However, the lack of extensive training of the DL model results in the reduced prediction of feature information and performance failure. Hence, to address the issues, in this paper, a combined DL-based optimal decoding model is proposed to improve the transmission error rate and enhance the overall efficiency of the RIS-assisted multiple-input multiple-output communication system. The proposed DL model is comprised of a 1-dimensional convolutional neural network (1-D CNN) and a gated recurrent unit (GRU) module where the 1-D CNN model is employed for the extraction of features from the received signal with further process over the configuration of different layers. Thereafter, the processed data are used by the GRU module for successively retrieving the transmission signal with a minimal error rate and accelerating the convergence rate. It is initially trained offline using created OFDM data sets, after which it is used online to track the channel and extract the transmitted data. The simulation results show that the proposed network performs better than the other technique that was previously used in terms of bit error rate and symbol error rate. The outcomes of the model demonstrate the suitability of the proposed model for use with the next-generation wireless communication system.

Free-space transmission of picosecond-level, high-speed optical pulse streams in the 3 µm band.

The utilization of mid-infrared (mid-IR) light spanning the 3-5 µm range presents notable merits over the 1.5 µm band when operating in adverse atmospheric conditions. Consequently, it emerges as a promising prospect for serving as optical carriers in free-space communication (FSO) through atmospheric channels. However, due to the insufficient performance level of devices in the mid-IR band, the capability of mid-IR communication is hindered in terms of transmission capacity and signal format. In this study, we conduct experimental investigations on the transmission of time-domain multiplexed ultra-short optical pulse streams, with a pulse width of 1.8 ps and a data rate of up to 40 Gbps at 3.6 µm, based on the difference frequency generation (DFG) effect. The mid-IR transmitter realizes an effective wavelength conversion of optical time division multiplexing (OTDM) signals from 1.5 µm to 3.6 µm, and the obtained power of the 40 Gbps mid-IR OTDM signal at the optimum temperature of 54.8 °C is 7.4 dBm. The mid-IR receiver successfully achieves the regeneration of the 40 Gbps 1.5 µm OTDM signal, and the corresponding regenerated power at the optimum temperature of 51.5 °C is -30.56 dBm. Detailed results pertaining to the demodulation of regeneration 1.5 µm OTDM signal have been acquired, encompassing parameters such as pulse waveform diagram, bit error rate (BER), and Q factor. The estimated power penalty of the 40 Gbps mid-IR OTDM transmission is 2.4 dB at a BER of 1E-6, compared with the back-to-back (BTB) transmission. Moreover, it is feasible by using chirped PPLN crystals with wider bandwidth to increase the data rate to the order of one hundred gigabits.