Error Ratio Research Articles

Nanoparticle Incorporation to Enhance Titanium Alloy Electric Discharge Machining Capabilities

The primary objective of this study was to examine the effects of the Powder-blended Micro Electric Discharge Machining (PMEDM) technique on micromachining applications, specifically when sea water is employed as the dielectric medium. Tiny apertures with a width of 200µm were punctured over Ti-6Al-4V plates. In the initial round of experimentation, the machining performance was evaluated by subjecting sea water to various process variables without the inclusion of any other ingredients. The effects of input variables, including electrode material, hole voltage, current, Pulse-on-time, and Duty factor, on the Material Removal Rate (MRR), Tool Wear Rate (TWR), Overcut (OC), Circularity Error (CE), and Taper Ratio (TR) were analysed by conducting tests in accordance with Taguchi's L18 plan design. The method employed to ascertain the optimal parametric configuration for multi-objective enhancement involved utilising the strategy of soliciting inclination based on similarity to an ideal arrangement. The present study aimed to investigate the effects of foreign materials on the dielectric-based miniature electrical discharge machining (EDM) process in sea water. This was achieved by utilising powders with varying weight concentrations and molecule sizes, including non-conductive (Al2O3), semi-conductive (SiC), and conductive (Al) powders. The experimental setup ensured that the variable boundaries were maintained at their ideal settings. The results indicate that the choice of tool has a notable influence on the functioning of micro EDM when sea water is employed as the dielectric, without the inclusion of extraneous particles. The performance metric of multi-objective execution in PMEDM is influenced by the conductivity of supplementary compounds. An 83.18 percent increase in Material Removal Rate (MRR) was observed when SiC added compounds were utilised in Powder Mixed Electrical Discharge Machining (PMEDM). Conversely, there was a drop in Tool Wear Rate (TWR) by 36.42 percent, Open Circuit voltage (OC) by 21.48 percent, Current Efficiency (CE) by 45.15 percent, and Tool Roughness (TR) by 22.87 percent.

Denoising of Raman Spectra Using a Neural Network Based on Variational Mode Decomposition, Empirical Wavelet Transform, and Encoder-Bidirectional Long Short-Term Memory

Raman spectroscopy has been widely applied in numerous fields including bioanalysis, disease diagnosis, and molecular recognition, owing to its unique advantages of being non-invasive, rapid, and highly specific. However, the acquisition of Raman spectral data is often susceptible to various noise interferences, such as shot noise from the internal detector and dark current noise from the instrument. As a result, the weak Raman signals typically become arduous to discern, which affects the localization and identification of characteristic spectral peaks. This study investigates variational mode decomposition (VMD), empirical wavelet transform (EWT), and integrated encoder-bidirectional long short-term memory (EBiLSTM) modules to propose a neural network algorithm for adaptive denoising of Raman spectra. By combining VMD and EWT, the Raman spectra are decomposed into several sub-sequences, which solves the problem of mode mixing between high-frequency signals and noise in empirical mode decomposition, and significantly reduces the complexity of the original Raman spectral lines. The correlation coefficient between each modal component and the original signal is calculated along with the zero-crossing rate index to categorize the noise and signal sequences. Leveraging the linear differences between the ideal spectral lines and the noisy spectral curves, an encoder-bidirectional long short-term memory (EBiLSTM) denoising network is constructed for hierarchical denoising to extract valid spectral feature information from the high-frequency components, realizing refined adaptive denoising of the Raman spectra. Industry standard objective evaluation metrics on the signal-to-noise ratio and root mean square error are utilized to conduct simulation experiments comparing state-of-the-art algorithms, including empirical mode decomposition, VMD, sliding window averaging, and wavelet thresholding. The experimental results demonstrate that the Raman spectral denoising algorithm combining variational mode decomposition and neural networks improves the denoising performance by 13.38% to 72%, exhibiting higher accuracy and reliability.

Si Mach-Zehnder Modulator for PAM-4, QAM-OFDM, and DMT Transmission at C-Band

A silicon (Si) Mach-Zehnder modulator (MZM) is demonstrated for 10-km SMF Link at C-band (1550 nm), which allows the pre-emphasized 4-level pulse amplitude modulation (PAM-4) data transmission at 50 GBaud (100 Gbit/s) per channel. By employing the pre-emphasis to the PAM-4 data over different links, the channel dispersion-induced power fading can be compensated. The PAM-4 data with improved spectral usage efficiency in limited bandwidth is employed to encode the Si MZM, and the maximal transmission capacities of 50 GBaud for back-to-back (BtB), 50 GBaud for 2-km SMF, and 26 GBaud for 10-km DSF under KP4-FEC criterion are respectively obtained for providing 100/100/52 Gbit/s data transmission with qualified bit error ratios of 1.5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> /2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> /1.4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> . The penalties on the sensitivity of the receiving power are 0.32 dB for 50-GBaud PAM-4 data after 2-km-SMF link and 0.64 dB for 26-GBaud PAM-4 data over 10-km DSF as compared to the BtB case. Such a scheme takes advantages including the lower power consumption with less circuit complexity than traditional push-pull driven operation, which certifies the 50-GBaud PAM-4 data capability per channel for inter-data-center applications. In addition, this Si MZM device can also modulate the pre-emphasized 16-QAM OFDM data at 140 Gbit/s and the bit-loaded DMT data at 153 Gbit/s.

Optimizing Instrument Transformer Performance through Adaptive Blind Equalization and Genetic Algorithms

In real-world scenarios, deviations in the frequency response of instrumentation transformers can lead to distorted harmonic measurements, highlighting the critical role harmonic measurement plays in assessing power quality. The blind channel equalization technique offers a potential solution to improve the frequency response of a large number of instrumentation transformers already installed in substations. These transformers were designed to accurately measure only the fundamental phasor component. Therefore, in order to use them for harmonic phasor measurement, methodologies for reducing frequency distortion must be applied. In this work, we propose a novel approach to improve the frequency response of the instrument transformer using adaptive blind equalization. The blind technique can compensate for distortions caused by voltage and current transducers without requiring prior knowledge of input signals or circuit characteristics. The proposed methodology uses a Linear Prediction Filter to convert the colored noise present at the channel output into white noise. Furthermore, a genetic algorithm is used to find a pole to cancel possible zeroes present in the frequency response of some transducers. The main advantage of blind equalization with the genetic algorithm is its independence, operating without clear information about the channel or the input signal. Through extensive experimentation, we demonstrate the effectiveness of the proposed methodology in significantly reducing the absolute error in ratio and phase caused by current and voltage transformers. Simulated and laboratory experiments are presented in this paper.

Employing higher density lower reliability weather data from the Global Historical Climatology Network monitors to generate serially complete weather data for watershed modelling

AbstractHydrological models require complete and accurate weather data time series to represent watershed‐scale responses adequately. The Global Historical Climatology Network (GHCN) is the most comprehensive weather database used in hydrological modelling studies globally. Since higher‐density, lower‐reliability precipitation measurements from private citizens collected by the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network data were integrated into the GHCN, hydrological modellers in the United States have access to a much greater amount of weather data. However, the benefit of using CoCoRaHS data has not been assessed. The objectives of this work were to develop a method for generating a complete weather data time series based on the combination of data from multiple GHCN monitors and to assess several methods for the estimation of missing weather data. Weather data from GHCN monitors located within a specific radius of a watershed were obtained and interpolated using three estimation methods (Inverse Distance Weighting (IDW), Inverse Distance and Elevation Weighting (IDEW) and Closest Station), creating a seamless time series of weather observations. To evaluate the performance of the methodologies, weather data obtained from each estimation method was used to force the Soil and Water Assessment Tool (SWAT) and Thornthwaite‐Mather models for 21 US Department of Agriculture‐Conservation Effects Assessment Project watersheds in different climate regions to simulate daily streamflow for 2010–2021. Except for three watersheds, all of the SWAT models had Nash‐Sutcliffe Efficiency above 0.5, the ratio of the root mean square error to the standard deviation of observations below 0.7, and percent bias from −25% to 25% with a satisfactory performance rating. IDEW and IDW performed similarly, and the Closest Station method resulted in the poorest streamflow simulation. A comparison with published SWAT model results further corroborated improved model performance using novel combined GHCN data with all Closest Station, IDW and IDEW methods.

The Reference Wideband Inductive Current Transformer

The aim of this paper is to show that the developed inductive current transformer may ensure the required wideband transformation accuracy and it may be applied, as the reference source, in the measuring system for the evaluation of the transformation accuracy of inductive current transformers for harmonics of distorted current. This device ensures 5 A and 1 A RMS secondary currents to provide the opportunity to use the differential measuring setup. Such solutions are characterized by the significantly reduced measurement uncertainty in relation to the comparative measurements made between two current/voltage channels. The problems required to ensure the high wideband transformation accuracy, including the self-generation phenomenon of the low order higher harmonics to the secondary current and a too-low frequency range of operation, were overcome in the design process. The values of its ratio error and the phase displacement of the developed reference wideband inductive current transformer did not exceed ±0.2%/° up to 1 kHz, ±0.4%/° from 1 kHz up to 1.5 kHz and ±0.5%/° from 1.5 kHz up to 3 kHz, as is required to perform the test procedure in accordance with the optional requirements for the inductive current transformers defined in the new edition of the standard IEC 61869-1.

Image Steganography Using LSB and Hybrid Encryption Algorithms

In today’s era of widespread web technology and cloud computing, ensuring data security has become a crucial concern across various industries. Instances of data breaches and vulnerabilities in cloud storage have emphasized the need for robust data protection and communication protocols, particularly in sectors like social media, military, and research. This research proposes a Multi-Level Steganography (MLS) algorithm that employs two encryption algorithms, AES and Blow-Fish, to secure the cover image and embed encryption keys as key images within the stego image. The proposed MLS algorithm incorporates a robust pixel randomization function to enhance the security of the encrypted data. Experimental results demonstrate that the proposed algorithm effectively protects data with high Peak Signal-to-Noise Ratio (PSNR) and low Mean Square Error (MSE) values, ensuring superior image quality, reliable encryption, and decryption of secret messages. The utilization of hybrid encryption with AES and BlowFish algorithms further strengthens the algorithm’s security by augmenting the complexity of the encryption process.

Compensating Chromatic Dispersion and Phase Noise using Parallel AFB-MBPS For FBMC-OQAM Optical Communication System

Filter Bank Multi-Carrier Offset-QAM (FBMC-OQAM) is one of the hottest topics in research for 5G multi-carrier methods because of its high efficiency in the spectrum, minimal leakage in the side lobes, zero cyclic prefix (CP), and multiphase filter design. Large-scale subcarrier configurations in optical fiber networks need the use of FBMC-OQAM. Chromatic dispersion is critical in optical fiber transmission because it causes different spectral waves (color beams) to travel at different rates. Laser phase noise, which arises when the phase of the laser output drifts with time, is a major barrier that lowers throughput in fiber-optic communication systems. This deterioration may be closely related among channels that share lasers in multichannel fiber-optic systems using methods like wavelength-division multiplexing with frequency combs or space-division multiplexing. In this research, we use parallel Analysis Filter Bank (AFB) equalizers in the receiver part of the FBMC OQAM Optical Communication system to compensate for chromatic dispersion (CD) and phase noise (PN). Following the equalization of CD compensation, the phase of the carriers in the received signal is tracked and compensated using Modified Blind Phase Search (MBPS). The CD and PN compensation techniques are simulated and analyzed numerically and graphically to determine their efficacy. To evaluate the FBMC's efficiency across various equalizers, 16-OQAM is taken into account. Bit Error Rate (BER), Optical Signal-to-Noise Ratio (OSNR), Q-Factor, and Mean Square Error (MSE) were the primary metrics we utilized to evaluate performance. Single-tap equalizer, multi-tap equalizer (N=3), ISDF equalizer with suggested Parallel Analysis Filter Banks (AFBs) (K=3), and MBPS were all set aside for comparison. When compared to other forms of Nonlinear compensation (NLC), the CD and PN tolerance attained by Parallel AFB equalization with MBPS is the greatest.

Reconfigurable all-optical pattern-matching system for phase modulation formats based on phase-sensitive amplification in highly nonlinear fiber

All-optical pattern-matching system is to compare the input data with the target sequence to implement the data filtering directly in the optical layer. However, for current all-optical pattern-matching systems, one pattern-matching system can only match one modulation format, which limits its application. In this paper, a reconfigurable all-optical pattern-matching system for phase modulation formats is proposed. Taking advantage of phase-sensitive amplification (PSA) in highly nonlinear fiber (HNLF), the system can match various phase modulation formats through optical switches configured by a controller. We first evaluate the feasibility of the proposed system for binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulation formats with the transmission rates of 100 Gbaud and 200 Gbaud. The results show that our proposed system can correctly recognize and locate the position of the target sequence. Next, we analyze the extinction ratio (ER), contrast ratio (CR), Q factor, and bit error rate (BER) by changing the optical signal-to-noise ratio (OSNR) values of BPSK and QPSK signals to evaluate the noise performance. The proposed reconfigurable all-optical pattern-matching system provides an effective method to recognize signals with different phase modulation formats, which is suitable for future dynamically reconfigurable optical networks.

Embedded Parallel Implementation of LDPC Decoder for Ultra-Reliable Low-Latency Communications

Ultra-reliable low-latency communications, URLLC, are designed for applications such as self-driving cars and telesurgery requiring a response in milliseconds and are very sensitive to transmission errors. To match the computational complexity of LDPC decoding algorithms to URLLC applications on IoT devices having very limited computational resources, this paper presents a new parallel and low-latency software implementation of the LDPC decoder. First, a decoding algorithm optimization and a compact data structure are proposed. Next, a parallel software implementation is performed on ARM multicore platforms in order to evaluate the latency of the proposed optimization. The synthesis results highlight a reduction in the memory size requirement by 50% and a three-time speedup in terms of processing time when compared to previous software decoder implementations. The reached decoding latency on the parallel processing platform is 150 μs for 288 bits with a bit error ratio of 3.410–9.

Effects of imprecisely defined parameters on heat and mass transfer in a vertical annular porous cylinder

The present research provides a numerical study of heat and mass transfer in a vertical annular porous cylinder with uncertainties. In this study, a 5% error in Raleigh number (Ra), Radiation parameter (Rd), and Buoyancy ratio (N) are considered. The coupled differential equations are transformed into algebraic equations using the fuzzy finite element method that is simplified with the fuzzy Gauss-Seidel iterative approach. A parameter study is performed with different cases and the sensitivity of the involved uncertainties is discussed. The sensitivity of a parameter suggests the stability of the system over the change in parametric values. If a parameter is more sensitive, then the system is unstable with a change in parametric values. Similarly, if a parameter is less sensitive, then the system is stable with a change in parametric values. When we take a single parameter as uncertain, it is found that Ra is more sensitive. Similarly, if two parameters are taken as uncertain, then it is observed that the combination of Ra and N becomes more sensitive. The overall study provides an approach to deduce the sensitivity of the system parameters. This procedure can also be used in solving many other science and engineering problems.

Optical adaptive power control based on atmospheric channel reciprocity for mitigating turbulence disturbances in free-space optics communication.

A continuous time-domain adaptive power model of transmitter optical and control algorithm based on atmospheric turbulence channel reciprocity are explored for mitigating the free-space optical communication (FSOC) receiver optical intensity scintillation and bit error rate (BER) deterioration. First, a transmitter optical adaptive power control (OAPC) system architecture using four wavelength optical signals based on atmospheric turbulence channel reciprocity is proposed, and electronically variable optical attenuator (EVOA) and erbium-doped fiber amplifier (EDFA) are employed as the main OAPC units for power adaptation. Moreover, a reciprocity evaluation model for gamma-gamma (G-G) continuous-time signals is generated using the autoregressive moving average (ARMA) stochastic process, which takes into account the delay time and system noise, and a reciprocity-based OPAC algorithm is proposed. Numerical simulations were also performed to analyze the signal reciprocity characteristics under different turbulence, noise, and sampling time mismatch at both ends, as well as the scintillation index (SI) performance under OAPC system operation. Simultaneously, the time-domain signals of continuous quadrature amplitude modulation -16 (QAM-16) and QAM-32 real states are fused with the gamma-gamma (G-G) reciprocal turbulence continuous signals to analyze the probability density function (PDF) and bit error ratio (BER) performance after OAPC correction. Finally, a 64 Gpbs QAM-16 OPAC communication experiment was successfully executed based on an atmospheric turbulence simulator. It is shown that the OAPC correction is carried out using reciprocity at millisecond sampling delay, the light intensity scintillation of the communication signal can be well suppressed, the signal-to-noise ratio (SNR) is greatly improved, the suppression is more obvious under strong turbulence, the overall BER reduction is greater than 2.8 orders of magnitude with the OAPC system, and this trend becomes more pronounced as the received power increases, even reach 6 orders of magnitude in some places. This work provides real time-domain continuous signal samples for real signal generation of communication signals in real turbulence environments, adaptive coding modulation using reciprocity, channel estimation, and optical wavefront adaptive suppression, which are the basis of advanced adaptive signal processing algorithms.

Metrological support of digital electronic voltage transformers and low-power voltage transformers

Metrological support of digital electronic voltage transformers and low-power voltage transformers (sensors) is represented. The methods and reference measuring instruments of the State primary special standard of units of the ratio error and the phase displacement of AC electric voltage of power frequency in the range from to and units of electric capacitance and tangent of loss dissipation factor at AC voltage of power frequency in the range from 1 to 500 kV – GET 175-2023, used for metrological support of the measuring instruments mentioned above. The results of research of GET 175-2023 are presented. The expanded uncertainties of GET 175-2023 when reproducing the units of the ratio error and the phase displacement of AC electric voltage of power frequency for metrological support of analog low-power voltage transformers (sensors) include the budget of the uncertainties of the current comparator, low-voltage (reference) and high-voltage measuring measures, the high-voltage MVE-01 bridge (SA7400MA1 amplifi er) and are respectively 9.62·10–5 and The expanded uncertainties of GET 175-2023 when reproducing the units of the ratio error and the phase displacement of AC electric voltage of power frequency for metrological support of electronic digital voltage transformers and digital low-power voltage transformers (sensors) include the budget of uncertainties of the converter «high voltage – current», converter «current – low voltage», converter «low voltage – a digital copy of voltage», a digital comparison device and constitutes 10.8·10–5 and respectively. GET 175-2023 allows solving the problems of metrological support of electronic digital voltage transformers and digital low-power voltage transformers (sensors).

Optimization of viscoelastic behaviors of bioinspired asymmetric helicoidal CFRP composites using Taguchi Method

The dynamic mechanical properties play an important role in the selection of suitable materials in the manufacturing wing of aircraft and wind turbine blades. In this paper, the standard Taguchi method was used to examine the effect of inter-ply stacking angles of 0° (UD), 0/90° (cross-ply), 5°, 15°, 10°, 20°, 25° 30° and aging temperature (ambient temperature, 40o, 60o) on the dynamic mechanical properties of bioinspired asymmetric helicoidal Carbon Fiber Reinforced Plastic (CFRP) composites. The standard Taguchi’s L18 was used. The signal-to-noise ratio and analysis of variance were introduced to analyze and estimate the optimal combination parameters. The results show the dynamic mechanical properties are linearly correlated to the fiber architecture and aging temperature. Analysis of variance (ANOVA) indicates that inter-ply stacking angles (15°, and 20°) and aging temperature (40o, 60o) are the main significant factors affecting the dynamic mechanical values at a 95% confidence level. Inter-ply stacking angles are finally noted as critical factors affecting the extent of macromolecular mobility within helicoidally stacked continuous fiber CFRP composites. A confirmation test validated the optimized results and it was found that there were improvements in S/N ratios from initial to optimal setting. The experimental and expected results are very close, with an error ratio not exceeding 5%.

Data-driven real-time prediction for attitude and position of super-large diameter shield using a hybrid deep learning approach

The presented research introduces a novel hybrid deep learning approach for the dynamic prediction of the attitude and position of super-large diameter shields - a critical consideration for construction safety and tunnel lining quality. This study proposes a hybrid deep learning approach for predicting dynamic attitude and position prediction of super-large diameter shield. The approach consists of principal component analysis (PCA) and temporal convolutional network (TCN). The former is used for employing feature level fusion based on features of the shield data to reduce uncertainty, improve accuracy and the data effect, and 9 sets of required principal component characteristic data are obtained. The latter is adopted to process sequence data in predicting the dynamic attitude and position for the advantages and potential of convolution network. The approach’s effectiveness is exemplified using data from a tunnel construction project in China. The obtained results show remarkable accuracy in predicting the global attitude and position, with an average error ratio of less than 2 mm on four shield outputs in 97.30% of cases. Moreover, the approach displays strong performance in accurately predicting sudden fluctuations in shield attitude and position, with an average prediction accuracy of 89.68%. The proposed hybrid model demonstrates superiority over TCN, long short-term memory (LSTM), and recurrent neural network (RNN) in multiple indexes. Shapley additive exPlanations (SHAP) analysis is also performed to investigate the significance of different data features in the prediction process. This study provides a real-time warning for the shield driver to adjust the attitude and position of super-large diameter shields.

End-to-End Deep Learning of Joint Geometric Probabilistic Shaping Using a Channel-Sensitive Autoencoder

In this paper, we propose an innovative channel-sensitive autoencoder (CSAE)-aided end-to-end deep learning (E2EDL) technique for joint geometric probabilistic shaping. The pretrained conditional generative adversarial network (CGAN) is introduced in the CSAE which performs differentiable substitution of the optical fiber channel model under variable input optical power (IOP) levels. This enables the CSAE-aided E2EDL to design optimal joint geometric probabilistic shaping schemes for optical fiber communication systems at varying IOPs. The results of the proposed CSAE-aided E2EDL technique show that for a dual-polarization 64-Gbaud signal with a transmission distance of 5 × 80 km, when the modulation format is a 64-quadrature amplitude modulation (QAM) or a 128-QAM, the maximum generalized mutual information (GMI) level learned via CSAE-aided E2EDL is 5.9826 or 6.8384 bits/symbol under varying IOPs, respectively. In addition, the pretrained CGAN, as a substitution for optical fiber transmission model, accurately characterizes the distortion of signals with different IOPs, with an average bit error ratio (BER) difference of only 1.83%, an average mean square error (MSE) of 0.0041 and an average K-L divergence of 0.0046. In summary, this paper delivers new insights into the application of E2EDL and demonstrates the feasibility of joint geometric probabilistic shaping-based E2EDL for fiber optic communication systems with varying IOPs.

Reinforcement learning-based adaptive modulation scheme over underwater acoustic OFDM communication channels

The conventional adaptive modulation (AM) technology is restricted in its effectiveness within the rapidly changing time-space-frequency of the underwater acoustic (UWA) channel. In contrast, reinforcement learning (RL) based AM employs an online training mode that interacts with the channel environment, enabling optimal modulation and coding schemes. By utilizing channel information and considering the bit error ratio (BER) of signals, we propose an RL-based AM strategy that adapts the modulation scheme to enhance spectral efficiency in a non-stationary UWA environment and maximize data throughput for UWA communication. The research is centered on the application of multi-carrier modulation in orthogonal frequency division multiplexing (OFDM). The AM is formulated as a Markov decision process (MDP) and resolved through the implementation of the Proximal Policy Optimization (PPO) algorithm. Simulation experiments indicate that the PPO algorithm exhibits faster and more efficient convergence while maintaining a stable convergence effect, in comparison to the Deep Q-Network (DQN) algorithm, Double DQN (DDQN) algorithm, and Q-Learning (QL) algorithm when addressing the UWA modulation problem.

Performance analysis of a hybrid FSO/mmWave/THz system for short-range communication under rain and fog conditions

For fronthaul cellular networks, the suitability of a hybrid free space optical (FSO)/radio frequency (RF) high data rate communication systems such as millimeter wave (mmWave) and terahertz (THz) with reliable connectivity under weather conditions (rain and fog) is proposed and analyzed. The aim of this hybrid system is to suit short-range communication covering 5G/6G and beyond cellular networks. The 4 pulse amplitude modulation (PAM) scheme parallel transmission and adaptive receiver approach is considered for the work. The theoretical work on estimating the attenuation characteristics under various weather conditions is carried out followed by the simulation of a hybrid communication system for obtaining the received power at different link lengths. Based on the received power for a data rate of 1 Gbps signal-to-noise ratio (SNR) and bit error rate (BER) is calculated for a link length of 0.2 km, 0.5 km, and 1 km for 4 PAM. For varying fog conditions, mmWave/THz carrier is found to be suitable with a BER of 10−10 at a link length of 0.5 km. For varying rain rates, FSO performs well with a BER of 10−8. The link length and performance can be improved with increased transmit power in both communication systems. The THz carrier offers a high data rate, and a hybrid system is proposed with FSO/THz to have reliable connectivity in harsh weather scenarios.

Non-Contact Adaptive Voltage Sensor Based on Electric Field Coupling Principle.

Non-contact voltage sensors based on the principle of electric field coupling have the advantages of simple loading and unloading, high construction safety, and the fact that they are not affected by line insulation. They can accurately measure line voltage without the need to connect to the measured object. Starting from the principle of non-contact voltage measurement, this article abstracts a non-contact voltage measurement model into the principle of capacitive voltage sharing and deduces its transfer relationship. Secondly, it is theoretically inferred that the edge effect of the traditional symmetric structure sensor plate will cause the actual capacitance value between the sensor plates to be greater than the theoretically calculated capacitance value, resulting in a certain measurement error. Therefore, the addition of an equipotential ring structure is proposed to eliminate the edge additional capacitance caused by the edge effect in order to design the sensor structure. In addition, due to the influence of sensor volume, material dielectric constant, and other factors, the capacitance value of the sensor itself is only at pF level, resulting in poor low-frequency performance and imbuing the sensor with a low voltage division ratio. In this regard, this article analyzes the measurement principle of non-contact voltage sensors. By paralleling ceramic capacitors between the two electrode plates of the sensor, the capacitance of the sensor itself is effectively increased, improving the low-frequency performance of the sensor while also increasing the sensor's voltage division ratio. In addition, by introducing a single pole double throw switch to switch parallel capacitors with different capacitance values, the sensor can have different voltage division ratios in different measurement scenarios, giving it a certain degree of adaptability. The final sensor prototype was made, and a high and low voltage experimental platform was built to test the sensor performance. The experimental results showed that the sensor has good linearity and high measurement accuracy, with a ratio error of within ±3%.

Low Complexity Detection of Spatial Modulation Aided OTFS in Doubly-Selective Channels

A spatial modulation-aided orthogonal time frequency space (SM-OTFS) scheme is proposed for high-Doppler scenarios, which relies on a low-complexity distance-based detection algorithm. We first derive the delay-Doppler (DD) domain input-output relationship of our SM-OTFS system by exploiting an SM mapper, followed by characterizing the doubly-selective channels considered. Then we propose a distance-based ordering subspace check detector (DOSCD) exploiting the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information of the transmit symbol vector. Moreover, we derive the discrete-input continuous-output memoryless channel (DCMC) capacity of the system. Finally, our simulation results demonstrate that the proposed SM-OTFS system outperforms the conventional single-input-multiple-output (SIMO)-OTFS system, and that the DOSCD conceived is capable of striking an attractive bit error ratio (BER) vs. complexity trade-off.