Error Correction Function Research Articles

Distinct checkpoint and homolog biorientation pathways regulate meiosis I in Drosophila oocytes.

Mitosis and meiosis have two mechanisms for regulating the accuracy of chromosome segregation: error correction and the spindle assembly checkpoint (SAC). We have investigated the function of several checkpoint proteins in meiosis I of Drosophila oocytes. Evidence of a SAC response by several of these proteins is found upon depolymerization of microtubules by colchicine. However, unattached kinetochores or errors in biorientation of homologous chromosomes does not induce a SAC response. Furthermore, the metaphase I arrest does not depend on SAC genes, suggesting the APC is inhibited even if the SAC is silenced. Two SAC proteins, ROD of the ROD-ZW10-Zwilch (RZZ) complex and MPS1, are also required for the biorientation of homologous chromosomes during meiosis I, suggesting an error correction function. Both proteins aid in preventing or correcting erroneous attachments and depend on SPC105R for localization to the kinetochore. We have defined a region of SPC105R, amino acids 123-473, that is required for ROD localization and biorientation of homologous chromosomes at meiosis I. Surprisingly, ROD removal, or "streaming", is independent of the dynein adaptor Spindly and is not linked to the stabilization of end-on attachments. Instead, meiotic RZZ streaming appears to depend on cell cycle stage and may be regulated independently of kinetochore attachment or biorientation status. We also show that dynein adaptor Spindly is also required for biorientation at meiosis I, and surprisingly, the direction of RZZ streaming.

Short-term industrial load forecasting based on error correction and hybrid ensemble learning

Accurate industrial load forecasting is a prerequisite for ensuring the smooth operation of the power system. Due to the strong fluctuation and complex characteristics of industrial loads, it is difficult to accurately predict short-term power demand. To address this issue, this paper proposes a deep learning prediction model based on hybrid ensemble and error correction. The proposed model is divided into two phases: in the first phase, deep power features are extracted from multivariate data through a hybrid ensemble strategy consisting of Random Subspace, Boosting, Ensemble Pruning, and Multi-Objective Molecular Dynamics Theory Optimization Algorithm (MMDTOA). First, the strategy splits high-dimensional industrial data into multiple sub-datasets. Subsequently, for the features of each sub-dataset, the proposed MMDTOA is applied to perturb the parameters of GRU to generate base learning machines that balance accuracy and diversity. Finally, these base learning machines are integrated by kernel ridge regression stacking. Among them, the two-stage selection strategy and co-evolutionary strategy are embedded into the MMDTOA to enhance the optimization searching effect; in the second stage, a combined error correction strategy is proposed by utilizing the residual information in the prediction results. By combining the dynamic Gaussian error correction function and GRU error correction model, the prediction accuracy of the ensemble model is further improved; Experimental results on real Korean datasets show that the proposed method achieves a minimum value of 3.684% and 7.266% in NMAE and NRMSE, which outperforms eight comparative models, such as SVM, ELM, and CNN, with higher accuracy and robustness.

Exploring transcription processes when children with and without reading and writing difficulties produce written text using speech recognition

The aim of this study was to investigate composition and error-correction processes, and their relationship with production rate, in children, age 10-12, with and without reading and writing difficulties using speech-to-text (STT) to write expository texts in Swedish. Measures of individual abilities: working memory, spelling ability and decoding ability, and the ability to interact with the STT tool under optimal conditions (STT success rate) were collected.
 For both those with and without difficulties, neither working memory, nor spelling or decoding ability predicted burst length nor accuracy. Only a child’s STT success rate did predict accuracy during text composition. Further, none of the individual abilities predicted choice of error-correction modality (keyboard or STT) or error correction functionality. This indicates that the children’s behavior were independent of these abilities.
 Furthermore, production rate was significantly predicted by both burst length and accuracy, and by working memory, but not by error-correction behaviour, nor by spelling or decoding ability. This indicates that composing text using STT is a cognitively complex process placing heavy demands on working memory. Dictating more than one word at a time and combining STT and keyboard use were identified as two useful strategies that can be taught in STT instruction.

Bayesian Calibration of MEMS Accelerometers

This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical systems (MEMS) accelerometers. These devices have garnered substantial interest in various practical applications and typically require calibration through error-correcting functions. The parameters of these error-correcting functions are determined during a calibration process. However, due to various sources of noise, these parameters cannot be determined with precision, making it desirable to incorporate uncertainty in the calibration models. Bayesian modeling offers a natural and complete way of reflecting uncertainty by treating the model parameters as variables rather than fixed values. Additionally, Bayesian modeling enables the incorporation of prior knowledge, making it an ideal choice for calibration. Nevertheless, it is infrequently used in sensor calibration. This study introduces Bayesian methods for the calibration of MEMS accelerometer data in a straightforward manner using recent advances in probabilistic programming.

HCAR: A Hybrid-Coding-Aware Routing Protocol for Underwater Acoustic Sensor Networks

Because of the characteristics of the underwater acoustic sensor networks (UASNs), such as long propagation delay, narrow bandwidth, limited resources, and intermittent connectivity, the design of the routing protocol has always been the focus and difficulty in UASNs research. Most of the existing coding-based underwater routing protocols only use the error correction function of network coding to improve the reliability of data transmission, ignoring that the network coding can effectively optimize the performance of packet transmission. In this paper, we propose a hybrid coding-aware routing protocol (HCAR). HCAR introduces inter-flow network coding and designs a new routing framework that combines reactive routing with opportunistic routing. It exhaustively explores potential coding opportunities in the network and proactively creates coding opportunities during the entire routing process, which enables HCAR to take full advantage of the broadcast nature of the wireless medium, thereby effectively reducing the number of transmissions. In addition, we design a new encoding method that does not rely on opportunistic listening, and thus avoids introducing excessive delay and overhead. The simulation results show that HCAR can effectively optimize the routing problem of the networks with multiple source nodes and a single sink node, further reducing the transmission overhead while ensuring reliability. We also implemented the key mechanism of HCAR protocol to verify its feasibility through sea tests.

Deep Compressed Super-Resolution Imaging with DMD Alignment Error Correction

In the field of compressed imaging, many attempts have been made to use the high-resolution digital micromirror array (DMD) in combination with low-resolution detectors to construct imaging systems by collecting low-resolution compressed data to reconstruct high-resolution images. However, the difficulty of achieving micrometer-level alignment between DMD devices and detectors has resulted in significant reconstruction errors. To address this issue, we proposed a joint input generative adversarial network with an error correction function that simulates the degradation of image quality due to alignment errors, designed an optical imaging system, and incorporated prior imaging system knowledge in the data generation process to improve the training efficiency and reconstruction performance. Our network achieved the ability to reconstruct 4× high-resolution images with different alignment errors and performed outstanding reconstruction in real-world scenes. Compared to existing algorithms, our method had a higher peak signal-to-noise ratio (PSNR) and better visualization results, which demonstrates the feasibility of our approach.

Pension Assets Investments in the Nigerian Economy

This study looked into pension assets investments and its impact in the Nigerian economy. The investments includes quarterly reports of corporate debt securities, government bond securities, mutual funds’ investments, money market instruments, private equity funds and real estate securities from 2004 – 2020, and was sourced from the statistics database of the National Pension Commission, the Central Bank of Nigeria and the World data bank. The statistical measures used to analyze the data are the descriptive test, Unit root test, Co-integration test, Vector error correction, Causality and Impulse response function. The outcome of the analysis show that the variables were stationary after first differences were taken; and were also co-integrated at 2 lags indicating that both short and long run equilibrium relationship exist among the variables. From the vector error correction model, previous years’ deviations from long run equilibrium had a 0.09% speed of adjustment; leading to a short run equilibrium. The Causality test reveal that changes in government bond securities lead to changes in real estate securities and changes in money market investments leads to changes in government bond securities. In addition, the impulse response demonstrates that a shock to the system produces more negative responses than positive ones. The study thus recommends that the pension industry should vigorously create more awareness on the importance of employee pension plan; develop more e-channels to help rake in investible funds as well as develop more innovative products to support diversification of pension fund in different assets classes among others.

Bioenergetics model for the nonnative Redside Shiner

AbstractObjectiveRedside Shiner Richardsonius balteatus has expanded from its native range in the Pacific Northwest region of North America to establish populations in six other western states. This expansion has fueled concerns regarding competition between Redside Shiner and native species, including salmonids. We developed a bioenergetic model for Redside Shiner, providing a powerful tool to quantify its trophic role in invaded ecosystems and evaluate potential impacts on native species.MethodsMass‐ and temperature‐dependent functions for consumption and respiration were fit based on controlled laboratory experiments of maximum consumption rates and routine metabolic rates using intermittent‐flow respirometry, across a range of fish sizes (0.6–27.3 g) and temperatures (5–31°C). Laboratory growth experiments were conducted to corroborate model performance across different temperatures and feeding rates.ResultInitial bioenergetic simulations of long‐term growth experiments indicated large model error for predicted consumption and growth, and deviations from observed responses varied systematically as a function of daily consumption rate (J·g−1·d−1) and water temperature. A growth rate error correction function was developed and included in the bioenergetics model framework on a daily time step, resulting in decreased absolute model error in all experimental groups. Predicted values from the corrected model were highly correlated with observed values (; consumption = 0.97, final weight = 0.99) and unbiased. These results show that the optimal temperature for Redside Shiner growth (18°C) exceeds that of Pacific salmon Oncorhynchus spp. by 2–6°C under a scenario of high food availability and moderate food quality.ConclusionConsequently, increases in water temperature associated with climate change may favor growth and expansion of Redside Shiner populations, while negatively affecting some salmonids. The bioenergetics model presented here provides the necessary first step in quantifying trophic impacts in sensitive ecosystems where Redside Shiner have invaded or in ecosystems where anadromous salmonid reintroductions are being considered.

DNA computing with error correction function in cells for cancer diagnosis and targeted therapy

Great progress has achieved on DNA computing which attracted ever increasing interesting for cancerous diagnosis. However, errors are hard to be avoided in any data transmission which directly impacts the computational reliability. That is of great concern for DNA computing-based diagnosis while has been neglected. Herein, a conceptual DNA logic circuit with error correction function for liver cancer diagnosis was constructed by integrating redundant modules which can trace the result of logic operation and help to judge whether the final result is right or not. Furthermore, photothermal treatment function was integrated in the DNA logic circuit for targeted therapy in time. The developed strategy significantly improves diagnosis accuracy and reliability, presenting instructive application in biomedical engineering and clinic diagnosis and therapy.

A CNN–SVM MODEL USING IMU FOR LOCOMOTION MODE RECOGNITION IN LOWER EXTREMITY EXOSKELETON

Human activity intention is indispensable for wearable powered lower extremity exoskeleton such that ensuring the compliant control of the robot. Lots of researches have been done on gait phase detection, which served as a sub-module of locomotion mode recognition to support the follow-on task. Therefore, it is self-evident that locomotion mode recognition is of great importance. Many model-based recognition methods are usually applied in manual extraction of cumbersome features, such as the traditional neural network (NN), support vector machine (SVM), etc. In contrast, the feature mapping layer coming with the convolutional neural network (CNN) can effectively solve the above time-consuming problem. Given that the training of NN is prone to overfitting, SVM with optimal characteristics is considered. A hybrid CNN–SVM model is proposed to identify human locomotion modes by collecting multi-channel inertial measurement unit (IMU) signals and is integrated with the error correction function of the finite state machine (FSM). Therefore, the CNN–SVM model has great influence on the generalization performance and recognition accuracy. The recognition rates of five single locomotion modes and eight mixed locomotion modes reach 97.91% and 98.93%, respectively. The system meets the demand of real-time performance, and the recognition time exceeds 370[Formula: see text]ms on heel strike.

Intelligent Correction System of Students’ English Pronunciation Errors Based on Speech Recognition Technology

With the increasingly frequent international exchanges, English has become a common language for communication between countries. Under this research background, in order to correct students’ wrong English pronunciation, an intelligent correction system for students’ English pronunciation errors based on speech recognition technology is designed. In order to provide a relatively stable hardware correction platform for voice data information, the sensor equipment is optimised and combined with the processor and intelligent correction circuit. On this basis, the MLP (Multi-layer Perceptron) error correction function is defined; with the help of the known recognition confusion calculation results, the actual input speech error is processed by gain mismatch; and the software execution environment of the system is built. Combined with the related hardware structure, the intelligent correction system of students’ English pronunciation error based on speech recognition technology is successfully applied, and the comparative experiment is designed and the practical application value of the system is highlighted.

Underwater 3D Measurements with Advanced Camera Modelling

A novel concept of camera modelling for underwater 3D measurements based on stereo camera utilisation is introduced. The geometrical description of the ray course subject to refraction in underwater cameras is presented under assumption of conditions, which are typically satisfied or can be achieved approximately. Possibilities of simplification are shown, which allow an approximation of the ray course by classical pinhole modelling. It is shown how the expected measurement errors can be estimated, as well as its influence on the expected 3D measurement result. Final processing of the 3D measurement data according to the requirements regarding accuracy is performed using several kinds of refinement. For example, calibration parameters can be refined, or systematic errors can be decreased by subsequent compensation by suitable error correction functions. Experimental data of simulations and real measurements obtained by two different underwater 3D scanners are presented and discussed. If inverse image magnification is larger than about one hundred, remaining errors caused by refraction effects can be usually neglected and the classical pinhole model can be used for stereo camera-based underwater 3D measurement systems.

Intelligent Correction System of Students’ English Pronunciation Errors Based on Speech Recognition Technology

With the increasingly frequent international exchanges, English has become a common language for communication between countries. Under this research background, in order to correct students’ wrong English pronunciation, an intelligent correction system for students’ English pronunciation errors based on speech recognition technology is designed. In order to provide a relatively stable hardware correction platform for voice data information, the sensor equipment is optimized and combined with the processor and intelligent correction circuit. On this basis, the MLP (Multilayer Perceptron) error correction function is defined, with the help of the known recognition confusion calculation results, the actual input speech error is processed by gain mismatch, and the software execution environment of the system is built. Combined with the related hardware structure, the intelligent correction system of students’ English pronunciation error based on speech recognition technology is successfully applied, and the comparative experiment is designed the practical application value of the system is highlighted.

Link Redundancy Research and Reliability Analysis Based on Dual CAN Architecture

Aiming at the problem that special vehicles still need to ensure the reliability of vehicle-mounted bus communication in the long-term complex and strong interference communication environment, a link layer redundancy scheme based on dual CAN channels is proposed. By adding redundant modules and improving the redundancy mechanism, dual CAN controllers can not only realize the undisturbed switching of data communication in hot redundancy mode, but also realize the function of error correction and retransmission in cold redundancy mode. Finally, a mathematical model of the reliability of dual CAN redundancy is established through the Markov state transfer chain, and this model is compared with other models for analysis and evaluation to verify that its reliability is significantly improved.

Evaluation of an error correction function using correlation characteristics of spread codes in CDMA-based transmission methods

In previous researches, we have proposed novel modulation methods such as the CDMA-QAM method in which Code-Division Multiple Access (CDMA) and Quadrature Amplitude Modulation (QAM) are combined, and applied these methods for improvement of railway signalling systems. Although a rail is used as a transmission medium, there are severe conditions for transmission, such as strong attenuation at frequencies above about 10 kHz. To overcome this restriction, we attempted to improve the S/N characteristics in our proposed method. Specifically, we focused on correlation characteristics of spread codes which are used in CDMA. In this paper, we show that an error correction function that does not require additional bits for such correction can be realized by using the correlation characteristics. We evaluated the effectiveness of this correction method and conducted computer simulations in which the method was employed for CDMA-based transmission.

Application of DCT to problem of collision resolution in RFID SAW systems by using correlation method

The article is devoted to SAW RFID systems. A problem of collision resolution by using a correlation method is under consideration. Collision is resolves by means of two correlation features, by the correlation function of the signal and by the correlation function of the DCT from the signal. A program, which is implements such collision resolution algorithm, was written. A collision is creating by using a simulation model of RFID tag. The model can generate a response from tag with «own» or with a random code, or can generate a collision of responses with random codes, which may contain or not contain an «own» code. The results of the algorithm’s operation are presented for the cases of reading from two to ten tags simultaneously. In addition to the main function, the algorithm implements error-correcting coding and decoding functions. It allows eliminating errors in case when there is no collision of tags, but a level of channel noise is high. The error-correcting coding is implemented by Reed-Solomon codes.

Using Punctured Convolution Coding (PCC) for Error Correction in Chipless RFID Tag Measurement

This letter presents an error correction technique for chipless radio frequency identification (RFID) tags using punctured convolution coding (PCC). We use a frequency signature-based chipless RFID tag which can encode Tag IDs at different resonance frequencies. Due to the presence of unwanted interference during communication, the backscatter tag response undergoes an abrupt change in amplitude and a shift in frequency, resulting in missing bits or errors. Normally, error correction in RFID technology requires intelligent behavior on the tag and in the reader. Our proposed measurement technique abandons the need for an integrated circuit (IC) and memory on the tag. Alternatively, the error correction functionality is executed during the design and fabrication process in chipless RFID tags. The PCC-based chipless RFID tag is then detected by the detection technique based on the presence of tag resonance resembling logic “1” and “0,” which is then decoded using Viterbi decoder to obtain the original source data. The proposed technique can detect error and recover missing bits in the chipless RFID system.

Demonstration of Controlled-Phase Gates between Two Error-Correctable Photonic Qubits.

To realize fault-tolerant quantum computing, it is necessary to store quantum information in logical qubits with error correction functions, realized by distributing a logical state among multiple physical qubits or by encoding it in the Hilbert space of a high-dimensional system. Quantum gate operations between these error-correctable logical qubits, which are essential for implementation of any practical quantum computational task, have not been experimentally demonstrated yet. Here we demonstrate a geometric method for realizing controlled-phase gates between two logical qubits encoded in photonic fields stored in cavities. The gates are realized by dispersively coupling an ancillary superconducting qubit to these cavities and driving it to make a cyclic evolution depending on the joint photonic state of the cavities, which produces a conditional geometric phase. We first realize phase gates for photonic qubits with the logical basis states encoded in two quasiorthogonal coherent states, which have important implications for continuous-variable-based quantum computation. Then we use this geometric method to implement a controlled-phase gate between two binomially encoded logical qubits, which have an error-correctable function.

Research on the information transmission based on linear block code and temporal ghost imaging algorithm

In recent years, ghost imaging technology, which uses a barrel detector without spatial resolution to detect and reconstruct object information separately, has become an important research object in the field of quantum optics. In this paper, based on the concrete analysis of ghost imaging algorithm on the basis of information coding and transmission, the information coding transmission is completed by combining the error-checking and error-correcting function of linear block code coding and the precise transmission function of temporal ghost imaging. This paper studied and analyzed the information transmission mechanism based on temporal ghost imaging from three aspects: feasibility, robustness and bit error rate. Based on triangular wave signal information, the simulation and experimental analysis shows that the information coding transmission mechanism improves the difficulty of decoding the code by the attacker, greatly enhances the ability of anti-interference and anti-interception, reduces the bit error rate, realizes the accurate information coding transmission, and solves the problem of the current imaging algorithm which is not feasible and the degree of reduction is not high.

Application investigation and technological development of intelligent product in college English teaching

Through quantitative and qualitative research methods, this paper investigated the application of intelligent products in College English teaching in China and proposed its future technological development prospect based on application problem and both teachers’ and students’ practical needs. The survey shows that kekenet.com and Chinese Universities MOOC Network are the most popular intelligent products used by teachers and students due to effective intelligent monitor and rich resource support function etc. The application problems and product disadvantages include lack of intelligent monitoring and recording system, lack of error identification and correction function, without learning process evaluation and individualized learning guidance etc. The future improvements will be adding listening error analysis in listening intelligent product, adding speech identification and comparison function in speech teaching software, creating translation teaching platform and adding word-in-use technique for vocabulary teaching product etc.