Error Rate Research Articles

Ultra‐Low BER Encrypted Communication Based on Self‐Powered Bipolar Photoresponse Ultraviolet Photodetector

AbstractUnderwater visible light communication often faces risks such as susceptibility to external light source interference, signal distortion, and information leakage. The cuprous oxide modified gallium oxide nanorod arrays detector prepared in this study can generate bipolar photocurrents under dual‐wavelength ultraviolet light without external bias voltage. Based on it, the constructed self‐powered ultraviolet ASCII code communication system not only reduce power consumption but also significantly enhance anti‐interference capability. Furthermore, the developed solar‐blind ultraviolet AMI and HDB3 ternary code communication systems can realize non‐visible light secure communication and automatic error correction. Notably, the encrypted communication system assembled using the additive property of bipolar photocurrents can also significantly improve communication security and reliability, whose bit error rate is only 0.26‰. This achievement contributes to technical advancements in underwater channel encryption and provides valuable insights for the further application of encrypted communication coding techniques.

Joint PAPR reduction with channel estimation for OFDM-based VLC systems

Abstract This paper presents a new combination of channel estimation (CE) methods and peak-to-average power ratio (PAPR) reduction techniques for orthogonal frequency division multiplexing (OFDM)-based visible light communication (VLC) systems. We first propose iterative clipping and filtering to minimize the PAPR of optical OFDM signals. Then, we introduce a computationally efficient, high-performance channel estimation approach using block-type pilots with modified clipped signals. The proposed method integrates clipping with three distinct CE techniques, resulting in both significant PAPR reduction and enhanced bit error rate (BER) performance. Among the CE methods evaluated, the expectation-maximization (EM) technique provides better BER results compared to other approaches. Simulation results confirm that the combined use of clipping and advanced CE methods leads to notable improvements in PAPR reduction and BER performance for OFDM-based VLC systems.

"So You Made a Mistake" - The Path Forward Through Surgical Pathology Errors by Extreme Ownership and a Focus on the Patient.

Our goal for medicine is to make zero mistakes, yet the reality is that mistakes are an unfortunate part of medical practice. And when it comes to surgical pathology, it is a special case where the diagnostic "bottom line" is provided starkly and directly for all to see in the final diagnosis of the pathology report. When this diagnosis is wrong, particularly when it has serious adverse consequences for the patient, the resulting physical, mental, and emotional effects on patient, provider, pathologist, and health care system can be extremely serious. Head and neck surgical pathology, based on large second review-type studies, is a subspecialty area with average rates of major diagnostic error, but with potential for severely negative impacts on patients. Studies have shown between 1% and 7% major error rates for head and neck practice. How then, as the pathologist, can we react to and manage things when we have made a serious diagnostic mistake? Through personal experience over more than two decades, the hard-won answer is through extreme ownership and a focus on the needs of the patients, who, in the words of William J. Mayo, should have their "needs come first". The emotional impact on us as pathologists and on the clinicians we work with should also be acknowledged and managed. This article will serve as a thorough and open examination of these mistake scenarios and, focusing specifically on diagnostic errors, serve as a practical guide for what you can do, moving forward, to "make things right" to the best of your ability.

Accurate optimal quantum error correction thresholds from coherent information

Quantum error correcting (QEC) codes protect quantum information from decoherence as long as error rates fall below critical error thresholds. In general, obtaining thresholds implies simulating the QEC procedure using, in general, suboptimal decoding strategies. In a few cases and for sufficiently simple noise models, optimal decoding of QEC codes can be framed as a phase transition in disordered classical spin models. In both situations, accurate estimation of thresholds demands intensive computational resources. Here we use the coherent information of the mixed state of noisy QEC codes to accurately estimate the associated optimal QEC thresholds already from small-distance codes at moderate computational cost. We show the effectiveness and versatility of our method by applying it first to the topological surface and color code under bit-flip and depolarizing noise. We then extend the coherent information based methodology to phenomenological and quantum circuit level noise settings. For all examples considered, we obtain highly accurate estimates of optimal error thresholds from small, low-distance instances of the codes, in close agreement with threshold values reported in the literature. Our findings establish the coherent information as a reliable competitive tool for the calculation of optimal thresholds of state-of-the-art QEC codes under realistic noise models. Published by the American Physical Society 2024

Evaluating the Modified American Academy of Pediatrics Screening Algorithm for Critical Congenital Heart Disease.

In 2018, an expert panel recommended two key modifications to the most used algorithm for screening neonates for critical congenital heart disease (CCHD). Our aim was to evaluate the outcomes of the modified algorithm compared with those of the original algorithm in a real-world setting. We compared the performance characteristics of an original CCHD algorithm used to screen term neonates at a large hospital system between October 26, 2018, and October 15, 2020, and the recommended modified algorithm used between October 15, 2020, and June 30, 2022. We calculated sensitivity, specificity, false positive rate, proportion of false positives with non-CCHD illness, and error rates of test administration and interpretation for each algorithm. Sensitivity was not significantly different between the modified algorithm compared with the original algorithm (40.00 vs. 12.50%, p ≥0.99), but specificity was lower (and hence false positive rate was higher) in the modified algorithm (99.91 vs. 99.98%, p < 0.001). Despite a higher false positive rate in the modified algorithm, the proportion of false positives with significant non-CCHD illness was similar (36.47 vs. 28.57%, p ≥0.99), a finding that translated to an increase in the number of cases of significant non-CCHD illness detected (11 cases out of 32,178 screens vs. 2 cases out of 32,984 screens). Error rates of test administration and interpretation were similar between the two algorithms. In this limited study, the modified algorithm for CCHD screening using pulse oximetry had a higher false positive rate than that of the original American Academy of Pediatrics algorithm. However, this higher rate led to an increased overall number of cases detected of significant non-CCHD illness. · Experts recommend two changes to the American Academy of Pediatrics-endorsed CCHD screen.. · This study evaluates the new algorithm for screening.. · The new algorithm detects at least as many cases as the original.. · There was no significant difference in test sensitivity for CCHD.. · The new algorithm has a statistically higher false positive rate..

High-dimensional quantum key distribution using orbital angular momentum of single photons from a colloidal quantum dot at room temperature

High-dimensional quantum key distribution (HDQKD) is a promising avenue to address the inherent limitations of basic quantum key distribution (QKD) protocols. However, experimental realizations of HDQKD to date have relied on indeterministic photon sources that limit the achievable key rate. In this paper, we demonstrate a full emulation of a HDQKD system using a single colloidal giant quantum dot (gQD) as a deterministic, compact, and room-temperature single-photon source (SPS). We demonstrate a practical protocol by encoding information in a high-dimensional space (d = 3) of the orbital angular momentum of the photons. Our experimental configuration incorporates two spatial light modulators for encoding and decoding the spatial information carried by individual photons. Our experimental demonstration establishes the feasibility of utilizing high radiative quantum yield gQDs as practical SPSs for HDQKD. We also experimentally demonstrate surpassing the traditional d = 2 QKD capacity with comparable error rates, indicating a significant improvement in performance while maintaining reliability.

A deep learning approach for line-level Amharic Braille image recognition

Braille, the most popular tactile-based writing system, uses patterns of raised dots arranged in cells to inscribe characters for visually impaired persons. Amharic is Ethiopia’s official working language, spoken by more than 100 million people. To bridge the written communication gap between persons with and without eyesight, multiple Optical braille recognition systems for various language scripts have been developed utilizing both statistical and deep learning approaches. However, the need for half-character identification and character segmentation has complicated these systems, particularly in the Amharic script, where each character is represented by two braille cells. To address these challenges, this study proposed deep learning model that combines a CNN and a BiLSTM network with CTC. The model was trained with 1,800 line images with 32 × 256 and 48 × 256 dimensions, and validated with 200 line images and evaluated using Character Error Rate. The best-trained model had a CER of 7.81% on test data with a 48 × 256 image dimension. These findings demonstrate that the proposed sequence-to-sequence learning method is a viable Optical Braille Recognition (OBR) solution that does not necessitate extensive image pre and post processing. Inaddition, we have made the first Amharic braille line-image data set available for free to researchers via the link: https://github.com/Ne-UoG-git/Am-Br-line-image.github.io.

Using multivariate partial least squares on fNIRS data to examine load-dependent brain-behaviour relationships in aging.

Researchers implementing non-invasive neuroimaging have reported distinct load-dependent brain activity patterns in older adults compared with younger adults. Although findings are mixed, these age-related patterns are often associated with compensatory mechanisms of cognitive decline even in the absence of direct comparisons between brain activity and cognitive performance. This study investigated the effects of cognitive load on brain-behavior relationships in younger and older adults using a data-driven, multivariate partial least squares (PLS) analysis of functional near-infrared spectroscopy (fNIRS) data. We measured bilateral prefrontal brain activity in 31 older and 27 younger adults while they performed single and dual 2-back tasks. Behavioral PLS analysis was used to determine relationships between performance metrics (reaction time and error rate) and brain oxygenation (HbO) and deoxygenation (HbR) patterns across groups and task loads. Results revealed significant age-group differences in brain-behavior relationships. In younger adults, increased brain activity (i.e., increased HbO and decreased HbR) was associated with faster reaction times and better accuracy in the single task, indicating sufficient neural capacity. Conversely, older adults showed a negative correlation between HbR and error rates in the single task; however, in the dual task, they demonstrated a positive relationship between HbO and performance, indicative of compensatory mechanisms under the higher cognitive load. Overall, older adults' showed relationships with either HbR or HbO, but not both, indicating that the robustness of the relationship between brain activity and behavior varies across task load conditions. Our PLS approach revealed distinct load-dependent brain activity between age groups, providing further insight into neurocognitive aging patterns, such as compensatory mechanisms, by emphasizing the variability and complexity of brain-behavior relationships. Our findings also highlight the importance of considering task complexity and cognitive demands in interpreting age-related brain activity patterns.

Efficient Biomedical Waste Management: Optimizing Segmentation and Classification with EnU-Net-DNN-BMWC

&lt;p style="margin-bottom:8px;text-align:justify;"&gt;&lt;span style="font-family:&amp;quot;Times New Roman&amp;quot;,serif;font-size:12.0pt;line-height:200%;tab-stops:0in 21.3pt;"&gt;In India, biomedical waste (BMW) management is governed by strict regulations to mitigate health and environmental risks. It includes materials such as sharps, infectious wastes, pharmaceuticals, and non-hazardous items contaminated with potentially infectious substances. Proper management and disposal of BMW are critical to prevent environmental contamination and health risks.&amp;nbsp; This article introduces an Enhanced U-Network (EnU-Net) integrated with Deep Neural Network BMW Classification (EnU-Net-DNN-BMWC) to enhance accuracy in this critical task. Leveraging the U-Net framework with an Encoder-Decoder Network (EDN) and pixel-wise classification layer initially optimizes image segmentation. Bayesian functions mitigate segmentation uncertainties, while Content-Sensitive Sampling (CSS) refines pixel sampling to prioritize data-sparse regions. Data collected from G. Kuppuswamy Naidu Memorial Hospital and Kovai Medical Center and Hospital (KMCH) in Coimbatore over six months, categorizing sharps, infectious materials, pharmaceuticals, and non-hazardous waste, informs waste management strategies. Experimental validation using 100 biomedical waste images demonstrates EnU-Net-DNN-BMWC achieving good accuracy, surpassing standalone DNN-BMWC using Matlab 2022. The comparative analysis across different metrics such as accuracy, precision, F-measure, recall, error rate, and RMSE between DNN-BMWC and the proposed EnU-Net-DNN-BMWC framework were evaluated in this study, highlighting EnU-Net-DNN-BMWC's superior performance. Finally, this study underscores EnU-Net-DNN-BMWC’s efficacy in enhancing biomedical waste classification, crucial for sustainable waste management practices and regulatory compliance in healthcare settings.&lt;/span&gt;&lt;/p&gt;

Empirical Mode Decomposition Based Amplify and Forward Technique for Cooperative Cognitive Radio System

The rapid growth in the mobile industry due to increase in number of users accessing diverse services causes a high demand for radio spectrum. Nonetheless, the radio spectrum allocated for different wireless communication services is restricted. Cooperative Cognitive Radio (CCR) technique with Amplify and Forward (AF) relaying protocol used to address the problem suffers from noise amplification resulting in poor reception at the destination. Hence, in this paper, Empirical Mode Decomposition (EMD) based AF technique for CCR system was carried out to improve the performance of the existing CCR with AF relaying protocol. The transmitted signal from Primary User (PU) was received at the Secondary User (SU) where SU superimposed its own signal using Exclusive OR (XOR) rule. The combined signal from XOR was made to pass through EMD and amplified using AF by multiplying with the relay gain. The amplified signal was radiated to PU and SU receivers during the second hop transmission. The multiple copies of the receive signal at the SU receiver at different number of path (L = 2, 4) were combined at destination using Maximal Ratio Combiner (MRC). Mathematical expression for Bit Error Rate (BER) and Throughput (TP) were derived using the Probability Density Function (PDF) of the Nakagami-m fading distribution. Extensive simulations using MATLAB R2021a were employed to assess the effectiveness of the proposed technique and evaluated using BER and TP by comparing with the existing AF CCR. The EMD based AF technique proposed gave better performance with 75.2% reduction in BER and 21.86% increase in TP over the existing AF technique for CCR. The proposed technique can be deployed to improve the performance of cooperative cognitive radio system.

Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals.

Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI). The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%. This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease.

5 G new radio fiber-wireless converged systems by injection locking multi-optical carrier into directly-modulated lasers

The integration of fiber-optical wireless convergence with fifth generation new radio is crucial in building high-performance access networks. This approach not only provides high-transmission-rates but also ensures broad coverage, which is vital for future networks. Here we report fifth generation new radio fiber-wireless converged systems by injection locking multi-optical carrier into directly-modulated lasers. Data rates of 10 Gb/s, 20 Gb/s, and 40 Gb/s are achieved by direct modulation on directly-modulated lasers using 16-quadrature amplitude modulation-orthogonal frequency-division multiplexing signal. Through 25-km single-mode fiber, 1.5-km optical wireless, and 12-/22-/33-m millimeter-wave/sub-terahertz wireless integrated-media, 10-Gb/s/20-GHz, 20-Gb/s/60-GHz, and 40-Gb/s/100-GHz signals are transmitted with acceptably low bit error rates and error vector magnitudes, as well as distinct constellations. The successful transport of fifth generation new radio millimeter-wave and sub-terahertz signals at different carrier frequencies through fiber-wireless convergence demonstrates the potential of the system to meet the evolving requirement of next-generation communications.

VLC performance in underground vehicular tunnels

In recent years, the integration of intelligent transportation systems (ITS) has gained popularity as a means of enhancing the safety of roadways and underground tunnels and reducing traffic congestion. Given the fact that conventional radio frequency (RF) communication systems are vulnerable to significant limitations as a result of a variety of factors, including signal attenuation and interference, which affect their application, the emerging visible light communication (VLC) technology is an exciting potential candidate for facilitating wireless access in such environments. This study investigates the deployment of VLC systems in underground vehicular tunnels involving a handover strategy based on the software-defined network (SDN) approach, with the objective of addressing the fundamental challenges faced by communication systems in such scenarios. The Optisystem software is used to simulate and investigate the performance of the proposed system, which is based on orthogonal frequency division multiplexing (OFDM) technology in both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The simulated scenario is capable of achieving a data rate of 10 Gbps within a link range of 3 meters in the LOS approach. In the NLOS propagation model, a data rate of 2 Gbps can be attained without any error. The simulation results reveal a particular perspective on the viability of VLC systems in improving the communication infrastructure in underground vehicular tunnels and promoting efficient tunnel operations. The evaluation of the simulated system is conducted based on bit error rate (BER), signal-to-noise ratio (SNR), and the constellation diagram.

Word-Final /s/-/z/ Omission in Vietnamese English

Southeast Asian learners of English, including those from Vietnam, frequently omit word-final consonants in their English speech. Previous work on Vietnamese learners of English is limited, and errors are typically usually attributed to first-language transfer effects. No large-scale empirical study on Vietnamese learners has been carried out to aid the development of an evidence-based pedagogy. This study uses authentic spoken data to compare lexical and morphological word-final /s/ and /z/ in the speech of sixteen Vietnamese adult learners of English. We discuss the relative impact of frequency of use, whether the instance of a target /s/ or /z/ is in a root or bound morpheme, and whether the preceding phoneme is a consonant or vowel. An overall omission rate of 28.4% of expected instances was found. Morphological {-s} when it is preceded by a consonant has the highest error rate (50.7%). A multilevel binary logistic regression was performed to ascertain the relative effects. Morphological words containing /s/ or /z/ were significantly more likely to be pronounced with the /s/ or /z/ absent than lexical words containing a /s/ or /z/, as were those in clusters compared to those with a preceding vowel. The results indicate that phonological effects and morphological effects are stacked and not multiplicative and that the observed omission rates are not solely attributable to L1 transfer effects. Frequency of use is also highly correlated with accuracy.

PDC-YOLO: A Network for Pig Detection under Complex Conditions for Counting Purposes

Pigs play vital roles in the food supply, economic development, agricultural recycling, bioenergy, and social culture. Pork serves as a primary meat source and holds extensive applications in various dietary cultures, making pigs indispensable to human dietary structures. Manual pig counting, a crucial aspect of pig farming, suffers from high costs and time-consuming processes. In this paper, we propose the PDC-YOLO network to address these challenges, dedicated to detecting pigs in complex farming environments for counting purposes. Built upon YOLOv7, our model incorporates the SPD-Conv structure into the YOLOv7 backbone to enhance detection under varying lighting conditions and for small-scale pigs. Additionally, we replace the neck of YOLOv7 with AFPN to efficiently fuse features of different scales. Furthermore, the model utilizes rotated bounding boxes for improved accuracy. Achieving a mAP of 91.97%, precision of 95.11%, and recall of 89.94% on our collected pig dataset, our model outperforms others. Regarding technical performance, PDC-YOLO exhibits an error rate of 0.002 and surpasses manual counting significantly in speed.

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

ABSTRACTThe spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

An ambipolar single-charge pump in silicon

The mechanism of single-charge pumping using a dynamic quantum dot needs to be precisely understood for high-accuracy and universal operation toward applications to quantum current standards and quantum information devices. The type of charge carrier (electron or hole) is an important factor for determining the pumping accuracy, but it has been so far compared just using different devices that could have different potential landscapes. Here, we report measurements of a silicon ambipolar single-charge pump. It allows a comparison between the single-electron and single-hole pumps that share the entrance tunnel barrier, which is a critical part of the pumping operation. By changing the frequency and temperature, we reveal that the entrance barrier has a better energy selectivity in the single-hole pumping, leading to a pumping error rate better than that in the single-electron pumping up to 400 MHz. This result implies that the heavy effective mass of holes is related to the superior characteristics in the single-hole pumping, which would be an important finding for stably realizing accurate single-charge pumping operation.

The Beam Squint Effects in Antenna Arrays at Millimeter Bands

Beam squint phenomenon is considered one of the most drawbacks that limit the use of (mm-waves) array antennas; which causes significant degradation in the BER of the system. In this paper, a uniform linear array (ULA) system is exemplified at millimeter (mm-waves) frequency bands to realize the effects of beam squint phenomena from different directions on an equivalent gain response to represent the channel performance in terms of bit error rate (BER). A simple QPSK passband signal model is developed and tested according to the proposed antenna array with beam squint. The computed results show that increasing the passband bandwidth and the number of antenna elements, have a significant degradation in BER at the receiver when the magnitude and phase errors caused by the beam squint at 26 GHz with various spectrum bandwidths.

A Machine Learning and Deep Learning-Based Account Code Classification Model for Sustainable Accounting Practices

Accounting account codes are created within a specific logic framework to systematically and accurately record a company’s financial transactions. Currently, accounting reports are processed manually, which increases the likelihood of errors and slows down the process. This study aims to use image processing techniques to predict cash codes in accounting reports, automate accounting processes, improve accuracy, and save time. Deep learning embeddings from Inception V3, SqueezeNet, VGG-19, VGG-16, Painters, and DeepLoc networks were utilized in the feature extraction phase. A total of six learning algorithms, namely Logistic Regression, Gradient Boosting, Neural Network, kNN, Naive Bayes, and Stochastic Gradient Descent were employed to classify the images. The highest accuracy rate of 99.2% was achieved with the combination of the Inception V3 feature extractor and the Neural Network classifier. The results demonstrate that image processing methods significantly reduce error rates in accounting records, accelerate processes, and support sustainable accounting practices. This indicates that image processing techniques have substantial potential to contribute to digital transformation in accounting, helping businesses achieve their sustainability goals.

Comparing typing methods for uppercase input in virtual reality: Modifier Key vs. alternative approaches

Typing tasks are basic interactions in a virtual environment (VE). The presence of uppercase letters affects the meanings of words and their readability. By typing uppercase letters on a QWERTY keyboard, the layers can be switched using a modifier key. Considering that VE controllers are typically used in a VE, this input method can result in user fatigue and errors. Thus, this study proposed new alternative interactions for the modifier key input and compared their typing performance and user experience. In an experiment, 30 participants were instructed to type 10 sentences using different typing interaction methods (shift, long press, and double-tap) on a virtual keyboard in a VE. The typing speed, error rate, and number of backspace inputs were measured to compare typing performance. Upon the completion of the typing task, the usability, workload, and sickness associated with each typing method were evaluated. The results showed that the double-tap method exhibited significantly higher typing speed, error rate, ease of use, satisfaction, and workload. This result is consistent with those of previous studies demonstrating that selection tasks were more efficient with fewer hand movements. Thus, this study implies that the double-tap method can be considered as a potential typing interaction for the VEs instead of the traditional method using the shift as a modifier key. Therefore, this study is expected to contribute to the design and development of user-friendly interactions.