Error Ratio Research Articles

LoRA-LoRaWAN Communication Multinode for 3D Localization in Coastal Environment

The application of LoRAWAN on Internet of Things (IoT) technology is to communicate in real time and accommodate data from many nodes based on device addresses. LoRAWAN device communication is able to reach distances of up to kilometers with low cost, compared to high-frequency cellular communication currently installed on the coast. This application can be done on LoRA devices to forward the results of three-dimensional localization based on signal strength. This research was to conduct three-dimensional localization based on signal strength from three LoRa End Nodes (EN) to four LoRa Anchor Nodes (AN), then forwarded to the server to be displayed on the Datacakes application. Localization begins with a path loss model analysis to determine the path loss coefficient. The localization results were in the form of data packets consisting of longitude, latitude, and altitude position parameters and the results of the EN to AN distance conversion. The data packet was forwarded to the The Things Networks (TNN) server with Over The Air Activation (OTAA) activation mode. Root Mean Square Error (RMSE) analysis of the localization results for EN1 was 169.35 meters, EN2 was 395.08 meters and EN3 was 183.24 meters. The localization data packets were forwarded to the cloud server via the GW device. Analysis of GW communication with EN is shown by the Packet Error Ratio (PER), Air Time (AT), and latency parameters. The smallest PER results, fastest AT and lowest latency were obtained from GW communication with EN2, where the position of EN2 was closest to GW among the other ENs.

273.6 Tbit/s real-time S + C + L-band same-wavelength bidirectional wavelength division multiplexing transmission in anti-resonant hollow-core fiber.

Based on its ultra-low backscattering characteristic, anti-resonant hollow-core fiber is a good medium for bidirectional transmission, which means that the wavelengths of the transmission channels are the same in both directions. In this Letter, we present the first, to the best of our knowledge, demonstration of S + C + L-band same-wavelength bidirectional transmission in an anti-resonant hollow-core fiber. By using a commercial real-time coherent optical transceivers and amplified spontaneous emission (ASE) source, a total net throughput of 273.6 Tbit/s is achieved over 1.4 km nested anti-resonant nodeless fiber (NANF) with 19.65 THz bandwidth and bidirectional wavelength division multiplexing (WDM) transmission. Commercial 400 G, 1 T, and 800 G optical modules are employed in the S-band, C-band, and L-band, respectively. We experimentally evaluate the same-wavelength bidirectional WDM transmission performance by verifying 2 × 102 channels of 68 Gbaud probabilistically constellation-shaped (PCS) 16 quadrature amplitude modulation (QAM) in the S-band with 75 GHz grid, 2 × 48 channels of 118 Gbaud PCS-64QAM in a C-band with 125 GHz grid and 2 × 60 channels of 91.6 Gbaud PCS-64QAM in the L-band with 100 GHz grid. All 420 test channels could satisfy the bit error ratio (BER) requirements. The result shows great potential for same-wavelength bidirectional WDM transmission in an anti-resonant hollow-core fiber.

A method for calibrating robotic kinematic parameters based on a multi-error source model and an optimized measurement pose set

Purpose The purpose of this study is to improve the accuracy of industrial robot kinematic parameter identification and position accuracy by solving the problem of insufficient consideration of error sources in the kinematic parameter identification model and optimizing the selection of measurement pose set. Design/methodology/approach In this study, a kinematic calibration method for industrial robots considering multiple error sources is proposed. Based on the Modified Denavit Hartenberg (MD-H) model, a robot kinematics identification model including joint reduction ratio error, target ball installation error and coordinate system transformation error is established. Taking the optimal observability index O1 and the minimum flexible deformation as the optimization objectives, a measurement pose set optimization method based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to obtain a measurement pose set with higher identification accuracy. Findings Through experiments conducted with the Nantong Zhenkang ZK1400-6 robot as the test subject, the kinematic parameters identified by the optimized measurement pose set are more accurate than the randomly selected measurement pose set, and the positioning accuracy of the robot is improved from 2.11 to 0.31 mm, an increase of 85.3%. Originality/value This study introduces a position error model that comprehensively accounts for the error sources causing positioning inaccuracies. Building on this foundation, a novel flexible deformation index is proposed to quantify the flexible deformation in the measurement pose set, thereby reducing the impact of such deformation on the position error in the model. To the best of the authors’ knowledge, for the first time, this study presents an optimization method for the measurement pose set based on NSGA-II, using the flexible deformation index and observability index as objectives for multi-objective optimization, simultaneously optimizing the pose error and Jacobian matrix in the error model.

Microscopic augmented reality calibration with contactless line-structured light registration for surgical navigation.

The use of AR technology in image-guided neurosurgery enables visualization of lesions that are concealed deep within the brain. Accurate AR registration is required to precisely match virtual lesions with anatomical structures displayed under a microscope. The purpose of this work was to develop a real-time augmented surgical navigation system using contactless line-structured light registration, microscope calibration, and visible optical tracking. Contactless discrete sparse line-structured light point cloud is utilized to construct patient-image registration. Microscope calibration optimization with dimensional invariant calibrator is employed to enable real-time tracking of the microscope. The visible optical tracking integrates a 3D medical model with surgical microscope video in real time, generating an augmented microscope stream. The proposed patient-image registration algorithm yielded an average root mean square error (RMSE) of 0.78 ± 0.14mm. The pixel match ratio error (PMRE) of the microscope calibration was found to be 0.646%. The RMSE and PMRE of the system experiments are 0.79 ± 0.10mm and 3.30 ± 1.08%, respectively. Experimental evaluations confirmed the feasibility and efficiency of microscope AR surgical navigation (MASN) registration. By means of registration technology, MASN overlays virtual lesions onto the microscopic view of the real lesions in real time, which can help surgeons to localize lesions hidden deep in tissue.

Effects of short time -30° head-down tilt on time perception.

Exposure to microgravity induces abnormal experiences that may affect the perception of time. Head-down tilts (HDTs) are commonly used to investigate the effects of weightlessness. A -30° HDT is considered an appropriate model to simulate the acute phase of microgravity exposure. Temporal performance in a time reproduction task was assessed before and after 30 min of -30° HDT, using 800, 1,000, and 2,000 ms as standard intervals. Absolute error (AE), relative error (ratio), and coefficient of variation (CV) were calculated to quantify performance. Compared to baseline measures obtained prior to HDT, both the mean AE and the ratio were significantly increased after 30 min of -30° HDT at the 800 ms interval. A similar trend was observed at the 1,000 ms interval, but no significant effect was found at the 2,000 ms interval. No significant differences were observed in the CV before and after -30° HDT. Acute exposure to microgravity, simulated by the -30° HDT condition, primarily affects duration perception at sub-second intervals.

Prediction model for hot rolling force in electrical steel involving phase transformation using SSA-LSTM network and mathematical model

To enhance the rolling force prediction accuracy of non-oriented electrical steel involving phase transformation, a high-precision RFPM (rolling force prediction model) that combines the RFOM (rolling force optimisation model) with the SSA-LSTM (sparrow search algorithm and long short-term memory) network was established. The RFOM was developed by optimising the important influencing parameters, including the deformation resistance model in the phase separation region and stress state coefficient model, of rolling force. The SSA-LSTM network was proposed, which utilises the SSA to optimise the hyperparameters of the double-layer LSTM network, it enables learning the complex spatial data correlation of the rolling force and improves its prediction accuracy. The superiority of the proposed high-precision RFPM was verified, which adopts multiple evaluation indicators, by comparing the RFOM, the BP, the LSTM, and the SSA-LSTM models. The results manifest that the high-precision RFPM has better prediction performance, which has reached 94.40%, and it has better rolling force calculation accuracy, which the error ratio of δ≤5%, δ≤8%, and δ≤10% is 80.35%, 93.21%, and 97.14%, respectively. In addition, the proposed high-precision RFPM prediction time can meet the requirements of online real-time rolling force prediction. The rolling force accuracy of wide strips, especially non-oriented electrical steel involving phase transformation, can be significantly improved by optimising the factors affecting the rolling force of phase transformation electrical steel. The industrial data test shows that the high-precision RFPM based on the theoretical model and data-driven model can meet the requirements of on-site accuracy and online real-time control during the actual production in the large 1450 mm 4-high hot strip mills.

Experimental demonstration of 8190-km long-haul semiconductor-laser chaos synchronization induced by digital optical communication signal

Common-signal-induced synchronization of semiconductor lasers have promising applications in physical-layer secure transmission with high speed and compatibility with the current fiber communication. Here, we propose an ultra-long-distance laser synchronization scheme by utilizing random digital optical communication signal as the common drive signal. By utilizing the long-haul optical coherent communication techniques, high-fidelity fiber transmission of the digital drive can be achieved and thus ultra-long-distance synchronization is expected. Experiments were implemented with distributed feedback lasers injected by a random-digital phase-modulated drive light. Results show that high-quality synchronization can be achieved as the drive signal rate is larger than the laser relaxation frequency and the transmission bit error ratio is below a critical value. Chaos synchronization over 8191-km fiber transmission was experimentally achieved. Compared to traditional common-signal-induced synchronization using analog drive signal such as chaos, the distance is increased by 8 times, and complicated hardware devices for channel impairment compensation are no longer required. In addition, the proposed method does not sacrifice communication capacity like traditional methods which need a channel to transmit analog drive signal. It is therefore believed that this common-digital-signal induced laser synchronization paves a way for secure backbone and submarine transmission.

MAFA-Uformer: Multi-attention and dual-branch feature aggregation U-shaped transformer for sparse-view CT reconstruction

Background Although computed tomography (CT) is widely employed in disease detection, X-ray radiation may pose a risk to the health of patients. Reducing the projection views is a common method, however, the reconstructed images often suffer from streak artifacts. Purpose In previous related works, it can be found that the convolutional neural network (CNN) is proficient in extracting local features, while the Transformer is adept at capturing global information. To suppress streak artifacts for sparse-view CT, this study aims to develop a method that combines the advantages of CNN and Transformer. Methods In this paper, we propose a Multi-Attention and Dual-Branch Feature Aggregation U-shaped Transformer network (MAFA-Uformer), which consists of two branches: CNN and Transformer. Firstly, with a coordinate attention mechanism, the Transformer branch can capture the overall structure and orientation information to provide a global context understanding of the image under reconstruction. Secondly, the CNN branch focuses on extracting crucial local features of images through channel spatial attention, thus enhancing detail recognition capabilities. Finally, through a feature fusion module, the global information from the Transformer and the local features from the CNN are integrated effectively. Results Experimental results demonstrate that our method achieves outstanding performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE). Compared with Restormer, our model achieves significant improvements: PSNR increases by 0.76 dB, SSIM improves by 0.44%, and RMSE decreases by 8.55%. Conclusion Our method not only effectively suppresses artifacts but also better preserves details and features, thereby providing robust support for accurate diagnosis of CT images.

Exploring Illumination and Communication: A Comprehensive Analysis of LED Lighting in Modern Interior Architectural Designs, Enhanced by Weighted Sum Model and Cluster Analysis for Informed Lighting Selection

ABSTRACTThe study of light is crucial in modern interiors, as it dispels darkness and enhances both functionality and aesthetics. From the primal flicker of flames in ancient caves to the advanced light emitting diode (LED) systems of today, lighting has been pivotal in shaping our surroundings and experiences. In contemporary interior architectural designs, lighting fulfills not only functional roles but also greatly influences the aesthetics and atmosphere of a space. Light has been an integral element in sleek interior architecture. With the advancements in LED technology, the approach to illuminating spaces has been transformed providing energy‐efficient solutions. This paper explores the role of LED lighting in modern interior architectural designs focusing on its impact on illumination and communication. Using a weighted sum model and cluster analysis, the various LED lighting configurations are evaluated for their suitability in different spaces. Performance metrics such as illuminance levels, uniformity, adherence to standards, signal‐to‐noise ratio (SNR) and bit error rate (BER) are analyzed. By varying the configurations of LED array per position from 10 × 10 to 100 × 100 the performance across different scales is assessed. The results offer insights for selecting LED lighting options that optimize functionality, aesthetics and energy efficiency in modern interior spaces.

Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation

Glaucoma, the leading cause of irreversible blindness, must be diagnosed early and thus treated in time. However, it has no noticeable symptoms in its early stages and may not be detected easily. This paper aims to compare two well-known convolutional neural network (CNN) structures, namely Fully Convolutional Networks (FCNs) and U-Net for the segmentation of the optic disc (OD) and optic cup (OC) from retinal fundus images which play an important role in glaucoma diagnosis. The performance of both models is assessed using qualitative parameters such as the Dice coefficient, Jaccard index, and cup-to-disc ratio (CDR) error. In our experiment, the U-Net model yields more accurate segmentation results with 0.9601 average pixel accuracy and 0.9255 dice score for OD segmentation, outperforming the FCNs model with 0.9560 average pixel accuracy and 0.9132 dice score for OD segmentation. However, FCNs have a shorter inference time of 0. 0043 seconds against U-net’s 0. 0062 seconds making FCNs more suitable for real-time applications. The restrictions related to this study include biases from using only one dataset acquired from particular imaging devices, dependency on mask-based cropping techniques, and comparison being restricted to two fundamental architectures. This work presents the contribution of the deep learning models in improving glaucoma screening and therefore helping in avoiding blindness.

A comprehensive review of adaptive modulation and coding techniques for spectrum efficiency and interference mitigation in satellite communication

This paper provides a detailed analysis of Advanced Adaptive Modulation and Coding (AMC) techniques in satellite communication and compare it with the conventional frequency reuse system such as Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA), which rely on fixed resource allocation, AMC dynamically modifies modulation and coding rates, optimizing throughput during favorable conditions and ensuring reliability in adverse scenarios. In optimal conditions, spectral efficiency improves by 30-50% due to AMC's adaptability, as governed by Signal to Noise Ratio (SNR) and Bit Error Rate (BER) thresholds. This paper also demonstrates that its integration with machine learning (ML), specifically, deep learning (DL) and deep reinforcement learning (DRL) enables prediction and adaption, which can address challenges such as channel fading and dynamic signal variations in Low Earth Orbit (LEO) satellite network. Traditional methods are outperformed by these data driven approaches for further improving throughput and reliability. While AMC requires sophisticated hardware, complex algorithms, and efficient feedback systems, solutions such as predictive algorithms and hybrid approaches mitigate these challenges. These findings highlight AMC’s transformative role in addressing growing demands for higher data rates, efficient spectrum utilization, and robust communication, positioning it as a cornerstone for the future of satellite communication systems in dynamic environments.

Can all variations within the unified mask-based beamformer framework achieve identical peak extraction performance?

This study investigates mask-based beamformers (BFs), which estimate filters for target sound extraction (TSE) using time-frequency masks. Although multiple mask-based BFs have been proposed, no consensus has been reached on which one offers the best target-extraction performance. Previously, we found that maximum signal-to-noise ratio and minimum mean square error (MSE) BFs can achieve the same extraction performance as the theoretical upper-bound performance, with each BF containing a different optimal mask. However, two issues remained unsolved: only two BFs were covered, excluding the minimum variance distortionless response BF; and ideal scaling (IS) was employed to ideally adjust the output scale, which is not applicable to realistic scenarios. To address these issues, this study proposes a unified framework for mask-based BFs comprising two processes: filter estimation that can cover all possible BFs and scaling applicable to realistic scenarios by employing a mask to generate a scaling reference. Based on the operators and covariance matrices used in BF formulas, all possible BFs can be classified into 12 variations, including two new ones. Optimal masks for both processes are obtained by minimizing the MSE between the target and BF output. The experimental results using the CHiME-4 dataset suggested that 1) all 12 variations can achieve the theoretical upper-bound performance, and 2) mask-based scaling can behave like IS, even when constraining the temporal mean of a non-negative mask to one. These results can be explained by considering the practical parameter count of the masks. These findings contribute to 1) designing a TSE system, 2) improving scaling accuracy through mask-based scaling, and 3) estimating the extraction performance of a BF.

Analysis of Digital Television Signal Reception in Combined Transmitter Antenna Systems

The Indonesian broadcasting sector has undergone significant transformation with the implementation of the Analogue Switch Off (ASO) program. By July 2023, approximately 97.23% of television stations in Indonesia had migrated to digital broadcasting. However, this figure does not reflect an equitable distribution of normal broadcasts. Data from Transmission Station X indicate that 6.77% of broadcasts are blank, 12.24% experience freezing, and only 80.99% are classified as normal for channel X in the Jabodetabek region. These statistics suggest that the primary objective of the ASO program—providing an enhanced television viewing experience—has not yet been universally achieved. Therefore, efforts are required to ensure the equitable distribution of normal broadcasts. Transitioning from a lower transmitter antenna system to a combined transmitter antenna system is proposed as a potential solution. This study evaluates the Modulation Error Ratio (MER) values in the Jabodetabek region when channel X uses a combined transmitter antenna system. Measurements, conducted using the drive test method, reveal that 99.703% of MER data are above the threshold (normal broadcasts), 0.042% are at the threshold (freeze broadcasts), and 0.255% are below the threshold (blank broadcasts). These results demonstrate that adopting a combined transmitter antenna system can help address the uneven distribution of normal broadcasts in the Jabodetabek region.

CFO and PAPR Analysis for Single‐ and Multistream CIFDMA System

ABSTRACTThe paper investigates the performance of a cyclic interleaved frequency division multiple access (CIFDMA) system proposed in our earlier works. In our present study, the carrier frequency offset (CFO) and the peak‐to‐average power ratio (PAPR) analysis are done for the proposed CIFDMA, supporting single and multiple data stream transmissions. The proposed system is analyzed without employing any compensation technique needed to alleviate the effect of CFO and PAPR. The CIFDMA delivers superior performance over the conventional IFDMA system under low and moderate CFOs. Besides, the bit error ratio (BER) versus signal‐to‐noise ratio (SNR) performance comparisons for constant and random CFOs reveal the dominance of the proposed system over the conventional system for both single and multiple data stream transmissions. The PAPR performance of the proposed system is superior to the orthogonal frequency division multiple access (OFDMA) system. However, the PAPR of the proposed system escalates by an acceptable margin to that of the conventional system, and the deviation in the PAPR value falls within the acceptable range. The simulation results conclude that the proposed CIFDMA system will better replace the conventional IFDMA system for next‐generation communications.

Research on Waveform Adaptability Based on Lunar Channels

In recent years, the focus of space research and exploration by various countries and international space agencies has been on the return of humans to the moon. Astronauts on lunar missions need to utilize network communication and exchange data. Against this backdrop, it is necessary to consider the performance of communication systems and the extreme conditions of the lunar environment, such as signal attenuation and frequency selection, to ensure the reliability and stability of communication systems. Therefore, providing technical performance adapted to the lunar environment is crucial. In this article, we investigated the applicability of Orthogonal Frequency Division Multiple Access (OFDMA) and Single-Carrier Frequency Division Multiple Access (SC-FDMA) waveforms in the lunar communication environment. Specifically, we used Peak-to-Average Power Ratio (PAPR) and Bit Error Rate (BER) as performance indicators. By studying the impact of different modulation schemes and cyclic prefix lengths on communication performance, we completed the research on waveform adaptability based on lunar channels. Simulation results indicate that the transmission structure we designed can meet the system-level performance requirements of lunar communications. This research provides valuable insights for the design and optimization of communication systems for future lunar missions, paving the way for the seamless integration of advanced ground technologies in extraterrestrial environments.

Are We Estimating the Mean and Variance Correctly in the Presence of Observations Outside of Measurable Range?

Laboratory measurements used for safety assessments in clinical trials are subject to the limits of the used laboratory equipment. These limits determine the range of values which the equipment can accurately measure. When observations fall outside the measurable range, this creates a problem in estimating parameters of the normal distribution. It may be tempting to use methods of estimation that are easy to implement, however selecting an incorrect method may lead to biased estimates (under- or overestimation) and change the research outcomes, for example, incorrect result of two-sample test about means when comparing two populations or biased estimation of regression line. In this article, we consider the use of four methods: ignoring unmeasured observations, replacing unmeasured observations with a multiple of the limit, using a truncated normal distribution, and using a normal distribution with censored observations. To compare these methods we designed a simulation study and measured their accuracy in several different situations using relative error , ratio , and mean square errors of both parameters. Based on the results of this simulation study, if the amount of observations outside of measurable range is below 40%, we recommend using a normal distribution with censored observations in practice. These recommendations should be incorporated into guidelines for good statistical practice. If the amount of observations outside of measurable range exceeds 40%, we advise not to use the data for any statistical analysis. To illustrate how the choice of method can affect the estimates, we applied the methods to real-life laboratory data.

Research on Sports Injury Rehabilitation Detection Based on IoT Models for Digital Health Care.

Physical therapists specializing in sports rehabilitation detection help injured athletes recover from their wounds and avoid further harm. Sports rehabilitators treat not just commonplace sports injuries but also work-related musculoskeletal injuries, discomfort, and disorders. Sensor-equipped Internet of Things (IoT) monitors the real-time location of medical equipment such as scooters, cardioverters, nebulizer treatments, oxygenation pumps, or other monitor gear. Analysis of medicine deployment across sites is possible in real time. Health care delivery based on digital technology to improve access, affordability, and sustainability of medical treatment is known as digital health care. The challenging characteristics of such sports injury rehabilitation for digital health care are playing position, game strategies, and cybersecurity. Hence, in this research, health care IoT-enabled body area networks (HIoT-BAN) have been designed to improve sports injury rehabilitation detection for digital health care. The health care sector may benefit significantly from IoT adoption since it allows for enhanced patient safety; health care investment management includes controlling the hospital's pharmaceutical stock and monitoring the heat and humidity levels. Digital health describes a group of programmers made to aid health care delivery, whether by assisting with clinical decision-making or streamlining back-end operations in health care institutions. A HIoT-BAN effectively predicts the rise in sports injury rehabilitation detection with faster digital health care based on IoT. The research concludes that the HIoT-BAN effectively indicates sports injury rehabilitation detection for digital health care. The experimental analysis of HIoT-BAN outperforms the IoT method in terms of performance, accuracy, prediction ratio, and mean square error rate.

Simulation for the Influence of Energy-Shaping Laser on the Geometry of the Phase Change Zone during Laser Quenching of AISI 1045 Steel

In this paper, invoking the mechanism of laser phase change hardening, the presence of latent heat of phase change, the temperature-dependent fluctuation of the thermal coefficient, and the disparity of microstructure austenitizing during rapid heating and static heating are duly reckoned. The repercussion of laser energy density distribution on the geometric configuration of the laser quenching phase change zone is probed via a numerical simulation model. Concurrently, the self-developed laser shaper is employed to modify the energy allocation of the conventional laser, and laser quenching is executed on the surface of AISI1045 steel with the spotlight subsequent to the shaper. Through the comparison of the cross-sectional area of the experimental sample phase change zone and the simulated phase change zone, the projected error ratio of the model established in this research is less than 10%.

Demonstration of daytime CubeSat-to-ground laser communication in OOK modulation with a dual-mode superconducting nanowire single-photon detector

Single-photon detectors have been adopted in deep space laser communication to improve the sensitivity of the ground receiver. For low-earth-orbit (LEO) laser communication, a ground receiver with upgraded sensitivity offers several advantages, such as reducing the ground telescope aperture size for mobile applications, giving a large tolerance for atmosphere attenuation, and reducing the power requirement for the transmitter in space. On-off keying (OOK) is one of the most common intensity modulations in the LEO laser communications, but is not suitable for conventional single-photon detectors which output photon counting clicks. In this paper, we demonstrate an LEO cube-satellite (CubeSat) to ground communication with a dual-mode superconducting nanowire single-photon detector (SNSPD) that can both operate at single-photon counting regime and quasi-linear mode under 50 Mbps OOK modulation. The CubeSat-to-ground results show that the SNSPD can work well both during the day and at night, and cope effectively with the large fluctuation of optical power in satellite-to-ground links and the significant variation of background photon noise in a day. The optical power corresponding to bit error ratio less than 10−3 is estimated in a separate experiment, which is -45 dBm for low dark counts at night, and -35 dBm for high background noise in the daytime (∼10 Mcps upper limit). This work demonstrates the feasibility of adopting the modified dual-mode SNSPDs in the OOK format satellite-to-ground laser communications both at night and in the day.

Performance Improvement by FRFT-OFDM for Visible Light Communication and Positioning Systems

In indoor visible light communication (VLC) and visible light positioning (VLP) systems, the performance of conventional orthogonal frequency-division multiplexing (OFDM) schemes is often compromised due to the nonlinear characteristics and limited modulation bandwidth of light-emitting diodes, the multipath effect in enclosed indoor environments, and the relative positions of transmitters and receivers. This paper proposes an OFDM scheme based on the fractional Fourier transform (FRFT) to address these issues, demonstrating promising results when applied to indoor VLC and VLP systems. The FRFT, a generalization of the conventional Fourier transform (FT) in the fractional domain, captures information in both the time and frequency domains, offering greater flexibility than the FT. In this paper, we first introduce the computation method of the reality-preserving FRFT for an intensity modulation/direct detection VLC system and integrate it with OFDM to optimize system performance. By adopting FRFT-OFDM under the optimal fractional order, we enhance both the bit error ratio (BER) performance and positioning accuracy. Simulation results reveal that the FRFT-OFDM scheme with an optimized fractional order significantly improves the BER and positioning accuracy compared to the FT-OFDM scheme across most receiver positions within the indoor observation plane. For communication, the FRFT-OFDM scheme achieves over 6 dB Eb/N0 gain compared to the FT-OFDM scheme at a BER of 3×10−4 when the receiver is positioned at most locations in the room. For positioning, the FRFT-OFDM scheme enhances positioning accuracy by more than 1 cm relative to the FT-OFDM scheme at most locations in the room. Notably, both systems maintain the same computational complexity and spectral efficiency.