Error Control Algorithm Research Articles

Error-controlled algorithm for the cumulative distribution function of doubly non-central F distribution

The doubly non-central F distribution is a widely applied probability distribution. The cumulative distribution function (CDF) of the doubly non-central F distribution can be expressed using the infinite doubly series. In this paper, a CDF calculation method that allows for controlled precision is derived through the study of the infinite doubly series. By setting parameters, this method can provide an upper limit of error for the computed results, thereby allowing for precision control in the calculations. Experimental results demonstrate that this method achieves a good balance between computational precision and speed.

A Multichannel, Multipulse, Multiweight Block-Adaptive Quantization (3MBAQ) Algorithm Based on Space-Borne Multichannel SAR Doppler Domain A-BAQ

Amid the conflict between the current huge data volumes and the requirement for large-compression-ratio compression in space-borne multichannel SAR, this paper proposes a data compression method that uses the adaptive bit allocation BAQ method in the Doppler domain of space-borne multichannel SAR. Specifically, 3MBAQ, which denotes multichannel, multipulse, multiweight block-adaptive quantization, performs signal reconstruction through a Krieger filter to obtain the Doppler spectrum under azimuthal multichannel and multipulse conditions and then achieves nonuniform bit allocation by using the multiple weight contributions between subband spectra. Furthermore, large-compression-ratio compression can be realized in homogeneous scenarios by using the adaptive bit allocation BAQ method together with the improved bit error control (BEC) algorithm. In this paper, the effectiveness of the 3MBAQ method is verified on GF3 measured data. Moreover, compared to the SQNR and NMSE of the BAQ, A-BAQ, A-BAQ-BEC, MCBAQ-ASQ, and MCBAQ-BEC methods under the same conditions, it is shown that there is an optimal 1.3434 dB improvement in SQNR and a 0.0241 improvement in NMSE using the 3MBAQ algorithm, and the complexity of the algorithm for 3MBAQ is O(N2log(N)), which is within acceptable limits, clearly proving the superiority of the 3MBAQ method.

Scalar Relativistic Effects with Multiwavelets: Implementation and Benchmark.

The importance of relativistic effects in quantum chemistry is widely recognized, not only for heavier elements but throughout the periodic table. At the same time, relativistic effects are strongest in the nuclear region, where the description of electrons through a linear combination of atomic orbitals becomes more challenging. Furthermore, the choice of basis sets for heavier elements is limited compared with lighter elements where precise basis sets are available. Thanks to the framework of multiresolution analysis, multiwavelets provide an appealing alternative to overcoming this challenge: they lead to robust error control and adaptive algorithms that automatically refine the basis set description until the desired precision is reached. This allows one to achieve a proper description of the nuclear region. In this work, we extended the multiwavelet-based code MRChem to the scalar zero-order regular approximation framework. We validated our implementation by comparing the total energies for a small set of elements and molecules. To confirm the validity of our implementation, we compared both against a radial numerical code for atoms and the plane-wave-based code EXCITING.

High precision control of direct-drive permanent magnet linear motor with adaptive friction compensation

To improve the control precision and stability of Direct-Drive Permanent Magnet Linear Motor (DPMLM) with frictional nonlinearity and uncertain disturbances at a low speed, an adaptive robust integral sign error control algorithm based on friction compensation of dual nonlinear observer is proposed in this study. An improved LuGre friction model is established to describe the friction phenomena of the system. The dual nonlinear observer is designed to observe the internal frictional state of the model. A parameter adaptive law is designed to perform parameter estimation of structured uncertainty. Robust integral of the sign of the error (RISE) term is designed to overcome the frictional nonlinear disturbances. The friction nonlinearity and parameter uncertainty are compensated by feedforward compensation. The bounded stability of the proposed controller is proved by Lyapunov stability theory. As suggested by the experimental results, RMSE index decreased by 10.8% and the control precision was improved effectively by the proposed control algorithm at a low speed.

RingVKB: A Ring-Shaped Virtual Keyboard Using Low-Cost IMU

Wearable devices have been important components for ubiquitous computing. However, text input remains challenging on wearables due to the lack of a physical keyboard. In this paper, we propose a novel ring-shaped virtual keyboard system named RingVKB for convenient text input using low-cost IMUs available on any wearables. At the core of RingVKB are two novel designs: 1) A circular keyboard layout with 12 equal sectors, which assembles all common keys on classical keyboards while allowing users to type with only one finger effectively, and 2) an error control algorithm that calculates the relative displacement of keystrokes from the noisy IMU sensor data. The two components, coupled together, enable high-accuracy and efficient text input for ubiquitous scenarios.We implement RingVKB using a small device consisting of a microcontroller and a MEMS sensor, which can be attached to the user's index finger.Experimental results show that RingVKB can effectively improve the relative displacement estimation accuracy, and achieves an overall keystroke recognition accuracy of 93% for 25 key positions. A user study also shows that RingVKB is easy to learn and use. Using only low-cost IMU sensors, RingVKB provides a virtual keyboard solution that can be widely adopted on wearables.

Intelligent electric vehicle trajectory tracking control algorithm based on weight coefficient adaptive optimal control

The track tracking effect of intelligent vehicle directly affects the safety of vehicle and passengers. In the process of intelligent vehicle track tracking, the track tracking accuracy is related to many factors such as track curvature, road friction coefficient, and longitudinal speed change. In this paper, a new lateral and longitudinal coupling control algorithm is proposed. Based on the vehicle dynamics model and the optimal control theory, combined with the fuzzy control theory, the Fuzzy Linear Quadratic Regulator (FLQR) lateral optimal controller is designed, and feed-forward control and predictive controller are added. According to the real-time tracking lateral error and fuzzy control algorithm fed back by the system, the weight coefficient of the lateral displacement deviation in the cost function is dynamically adjusted; considering the coupling effect of lateral and longitudinal controllers of vehicle trajectory tracking control, a model predictive control (MPC) longitudinal speed controller is designed based on MPC theory, considering acceleration constraint and acceleration variation constraint, and taking lateral stability as evaluation index. A joint simulation platform is built based on CarSim and Simulink. The simulation results show that the designed lateral and longitudinal coupling controller of FLQR + MPC has better track tracking accuracy and can improve the driving stability of the vehicle; finally, the tracking effect of the designed algorithm is verified by real vehicle experiments. The maximum error of the designed controller algorithm in real vehicle tracking is 0.56 m, and the tracking effect is good.

Prediction of Dementia Subtypes by Machine Learning: Feasibility of Error Control

AbstractBackgroundAccurate prediction of dementia subtypes is important in geriatric decision‐making and in designing clinical trials. We used machine learning and a novel error control algorithm to analyze longitudinal data collected by the National Alzheimer Coordinating Center (NACC) from June 2005 to November 2021. Our goals were (1) Predicting the conversion to several dementia subtypes in patients who were dementia‐free at baseline; (2) Finding important clinical metrics and risk factors that contribute to the prediction; (3) Controlling the prediction error rate on specific dementia subtypes to help design clinical trials.MethodWe studied the 13,243 NACC participants who had normal clinical etiologic diagnosis results at their first visit. Then, we fit different machine learning models to predict the diagnosis result of their last visit. Among 13,243 patients, 10,679 remained dementia‐free, 1,793 had Alzheimer’s disease (AD), 153 had Lewy body disease (LBD), and 290 had Vascular dementia (VD) as the primary diagnosis at their last exam. After removing predictors with over 10% missing values and imputing missing values on the remaining predictors, our models tested 249 predictors, which included clinical, family history, and behavioral features. Cases were randomly split the data into training (90%) and testing (10%) subsets for 500 rounds. Multinomial regression, penalized multinomial regression, and random forest models were analyzed. We further manipulated the penalized multinomial regression model by controlling the prediction error rate on a given dementia subtype.ResultMultinomial regression model, penalized multinomial regression model and random forest led to the overall prediction error rate of 10.73%, 9.26%, and 9.38%, respectively. Significant predictors included the length of time from the initial visit to the most recent visit, the cognitive status of the patient in the visit, and metrics of memory deficits. Error control results (Figure 1) showed that relaxing the strict control on some dementia subtypes serves to improve the prediction performance of the other dementia subtypes.ConclusionMachine learning methods can help predict the progression to dementia using commonly available variables available in memory clinics even in the absence of imaging and fluid biomarkers. ML is helpful in identifying most important clinical features and key risk factors.

Research progress on wireless sensor network (WSN) security technology

Abstract Wireless sensor network (WSN) has been a frontier technology field that integrates a variety of technologies and has a wide range of applications in the military. Network security is of great significance in military warfare. First, this paper outlines the technical advantages of the application of military based on the technical characteristics of WSN. In military applications, WSN has been used in modern military combat while WSN features easy and fast deployment, high concealment, good fault tolerance, high accuracy and other obvious advantages. Moreover, from the perspective of network security, this paper reviews the research progress of network security technology at the network level and data level. The network level includes security protocols, key management, and secure routing technology, and key management includes pre-shared key mode and random key pre-distribution mode. At the data level, error control and fault-tolerant algorithms are mainly included. Automatic retransmission and forward error correction techniques are traditional error control methods, which should be combined with the specific situation of WSN to propose the corresponding fault-tolerant mechanism. Finally, this paper summarizes the practical applications of WSN in the military. From battlefield reconnaissance and target localization monitoring to destruction effect control and anti-terrorism equipment research, network security has been a top priority. Against the background of practical military applications, this paper summarizes the research significance of network security technology.

Comprehensive Design Method of a High-Frequency-Response Fast Tool Servo System Based on a Full-Frequency Error Control Algorithm.

With the development of optoelectronic information technology, high-performance optical systems require an increasingly higher surface accuracy of optical mirrors. The fast tool servo (FTS) based on the piezoelectric actuator is widely used in the compensation machining of high-precision optical mirrors. However, with the low natural frequency of mechanical structures, hysteresis of the piezoelectric actuators, and phase delay of the control systems, conventional FTS systems face problems such as a low working frequency and a large tracking error. This study presents a method for the design of a high-performance FTS system. First, a flexure hinge servo turret with a high natural frequency was designed through multi-objective optimization and finite element simulations. Subsequently, a composite control algorithm was proposed, targeting the problems of hysteresis and phase delay. The modified Prandtl–Ishlinskii inverse hysteresis model was used to overcome the hysteresis effect and a zero-phase error tracker was designed to reduce the phase error. The experimental results reveal that the tracking error of the designed FTS system was <10% in the full frequency range (0–1000 Hz).

Error-control algorithm of voice messages transmission

High-speed and high-quality communications are required to ensure a smooth process of timely receipt and processing of information. The speed of obtaining information depends on the speed of response and timely management decisions. The development of information and telecommunication technologies has led to an increasing integration of communication and voice messaging systems implementing information processes in the control system. In communication systems where it is important to ensure the integrity, availability and confidentiality of transmitted information when low-bandwidth communication channels are used, voice information is encoded by special compression algorithms. Prefix codes are common in various fields of information technology. Many information compression algorithms are based on them, and they are used by different protocols. The use of run-length coding will reduce the speed of a transmitted signal and increase the throughput.

A novel curvature circle iterative algorithm for contour error control of multi-axis CNC machine tools

High-precision real-time estimation of contouring errors is a prerequisite for contouring errors control of multi-axis CNC machine tools. This paper focuses on developing a nearest point projection curvature circle iterative (NPP–CCI) algorithm to achieve real-time estimation of multi-axis contouring errors. It is found that the traditional curvature circle iterative (CCI) method has two major shortcomings. The first is that the iterative process may terminate incorrectly at the local contour position, and the other is that the actual tool position and local curvature circle are not necessarily coplanar in three-dimensional space, which would lead to inaccurate calculation of the delay time parameter and eventually affect the estimation accuracy. In order to address the problem of false termination, an index method is used to find the closest reference position with respect to the actual position. At the same time, the projection technology is proposed to overcome the problem met in extending the planar curvature circle iterative method to the spatial applications. The proposed NPP-CCI algorithm is more suitable for spatial contouring errors estimation in tracking complex trajectories and has higher estimation accuracy than the traditional CCI algorithm. Various experiments with different tool paths are conducted on an in-house developed multi-axis experimental platform to verify the effectiveness of the proposed algorithm. The experimental results show that the NPP-CCI algorithm can estimate the contouring errors with higher accuracy than the traditional CCI algorithm, and with the help of real-time computation and compensation, the contouring errors are reduced by more than 44% in terms of the MAX and RMS values.

Multiple error correction towards optimisation of energy in sensor network

&lt;span&gt;Hybrid ARQ (HARQ) is among the optimum error controls implemented in Wireless Sensor Network as it reduces the overhead from retransmission and error correcting codes. The advancement in WSN includes the usage of high number of nodes and the increase in traffic with large data transmitted among the nodes had concerned the need for a new approach in error control algorithm. This paper proposed the multiple error correction based on HARQ process to aid the changes in channel with proper error correction assignment towards optimising the performances of WSN in terms of bit error rates, remaining energy, and latency for different types of congestion and channel conditions. In this study, we have developed the channel adaptation algorithm that can adapt to sudden changes and demonstrated the optimal error correcting codes as well as adjustment on the transmit power for the given channel condition and congestion presented. From the result analysed, the optimisation between the remaining energy and Bit Error rates happened on the basis of adapting to these different channel condition and congestion to minimize redundancies appended. From the result obtained, we concluded that by using multiple error correction algorithm with the aid of adjustment on the transmit power, the remaining energy can be optimised together with Bit Error rates and the excessive redundancies can be reduced&lt;/span&gt;

Performance analysis of error-control B-spline Gaussian collocation software for PDEs

B-spline Gaussian collocation software has been widely used in the numerical solution of boundary value ordinary differential equations (BVODEs) and 1D partial differential equations (PDEs) for several decades. Such packages represent the numerical solution in terms of a piecewise polynomial (B-spline) basis with basis coefficients determined through the use of Gaussian collocation. The software package, BACOL, developed over a decade ago, was the first 1D PDE package of this type to provide both temporal and spatial error control. A recently developed package, BACOLI, improves upon the efficiency of BACOL through the use of new types of spatial error estimation and control. The complexity of the interactions among the component numerical algorithms used by these packages (particularly the spatial and temporal error estimation and control algorithms) implies that extensive testing and analysis of the test results is an essential factor in the ongoing development of these packages In this paper, we investigate the performance of BACOL and BACOLI with respect to several important machine independent algorithmic measures, examine the effectiveness of the new spatial error estimation and control strategies, and investigate the influence of the choice of the degree of the B-spline basis on the performance of the solvers. These results will provide new insights into how to improve BACOLI, potentially lead to improvements in Gaussian collocation BVODE solvers, and guide further development of B-spline Gaussian collocation software with error control for 2D PDEs.

A new efficient error control algorithm for wireless sensor networks in smart grid

Error detection and correction is an important issue in the design and maintenance of a smart grid communication network to provide reliable communication between sender and receiver. Various error-control coding techniques are employed to reduce bit error rates (BER) in wireless sensor networks (WSNs). The performance of these techniques is also compared and evaluated to find the most suitable technique for WSNs. This is the first study to compare the most efficient coding techniques in the smart grid environment, and it suggests a new error correction algorithm based on this comparison result. Therefore, this article first examines and compares two forward error control (FEC) coding techniques such as Bose-Chaudhuri-Hochquenghem code (BCH) and Reed Solomon code (RS) with various modulation methods including frequency shift keying (FSK), offset quadrature phase-shift keying (OQPSK), and differential phase shift keying (DPSK) in a 500 kV line-of-sight (LoS) substation smart grid environment. Second, as a result of this comparison, a new adaptive error control (AEC) algorithm is proposed. Adaptive error control adaptively changes error correction code (ECC) based on the channel behavior that is observed through the packet error rate (PER) in the recent previous transmissions. The link-quality-aware capacitated minimum hop spanning tree (LQ-CMST) algorithm and the multi-channel scheduling algorithm are used for data transmission over the log-normal channel. Therefore, the performance of compared coding techniques and AEC are also evaluated when multiple channels are used during transmission. Further, AEC is compared with static RS and without-FEC methods based on performance metrics such as the throughput, BER, and delay in different smart grid environments, e.g., 500 kV Substation (LoS), underground network transformer vaults (UTV) (LoS), and main power control room (MPR) (LoS). Our simulation results indicate that the proposed AEC algorithm achieves better performances than all those techniques.

Distributed Systematic Network Coding for Reliable Content Uploading in Wireless Multimedia Sensor Networks.

Recently, the wireless sensor network paradigm is shifting toward research aimed at enabling the robust delivery of multimedia content. A challenge is to deliver multimedia content with predefined levels of Quality of Service (QoS) under resource constraints such as bandwidth, energy, and delay. In this paper, we propose a distributed systematic network coding (DSNC) scheme for reliable multimedia content uploading over wireless multimedia sensor networks, in which a large number of multimedia sensor nodes upload their own content to a sink through a cluster head node. The design objective is to increase the reliability and bandwidth-efficient utilization in uploading with low decoding complexity. The proposed scheme consists of two phases: in the first phase, each sensor node distributedly encodes the content into systematic network coding packets and transmits them to the cluster head; then in the second phase, the cluster head encodes all successfully decoded incoming packets from multiple sensor nodes into innovative systematic network coding packets and transmits them to the sink. A bandwidth-efficient and channel-aware error control algorithm is proposed to enhance the bandwidth-efficient utilization by dynamically determining the optimal number of innovative coded packets. For performance analysis and evaluation, we firstly derive the closed-form equations of decoding probability to validate the effectiveness of the proposed uploading scheme. Furthermore, we perform various simulations along with a discussion in terms of three performance metrics: decoding probability, redundancy, and image quality measurement. The analytical and experimental results demonstrate that the performance of our proposed DSNC outperforms the existing uploading schemes.

Proposed dynamic error control techniques for QoS improvement of wireless 3D video transmission

SummaryError control techniques like error resilience (ER) and error concealment (EC) are efficient techniques to ameliorate the lost macroblocks (MBs) in the 3D video (3DV) communication system. In this paper, we propose efficient and adaptive hybrid ER‐EC algorithms for 3DV transmission over error‐prone wireless channels. At the encoder, adaptive preprocessing ER mechanisms are proposed through using the context adaptive variable length coding entropy, slice structured coding modes, and explicit flexible macroblock ordering mapping. They are used to assist the suggested EC techniques at the decoder to accurately reconstruct the erroneous MBs and frames. At the decoder, an efficient postprocessing EC technique with multiproposition methods is proposed to dynamically select the convenient EC hypothesis method based on the size of the lost MBs, the faulty view, and the frame type. It conceals the received erroneous MBs of intra‐encoded and inter‐encoded frames of the transmitted 3DV by exploiting the temporal, spatial, and inter‐view correlations among frames and views. To further improve the decoded 3DV quality, a weighted overlapping block motion and disparity compensation technique is used to reinforce the performance of the suggested ER‐EC techniques. Experimental results on various 3DV streams prove that the suggested techniques have considerably acceptable subjective and objective 3DV performance. They achieve an improved average peak signal‐to‐noise ratio gain by almost 2.85 dB compared to the conventional error control algorithms at a packet loss rate = 40%.

Effective multi-stage error control algorithms for robust 3D video transmission over wireless networks

The Three-Dimensional Video (3DV) contains diverse video streams taken by different cameras around an object. Thence, it is an imperative assignment to fulfill efficient compression to match the future resource limitations, whilst preserving a decisive reception 3DV quality. The efficient 3DV communication over wireless networks has become a recent considerable hot issue due to the restricted resources and the presence of severe channel errors. The high-rate 3DV encoding and transmission over mobile or Internet are vulnerable to packet losses due to the existence of heavy channel losses and limited bandwidth. Therefore, this paper presents efficient multi-stage error control algorithms for reliable 3DV transmission over error-prone wireless channels. At the encoder, the error resilience schemes of context adaptive variable length coding, slice structured coding, and explicit flexible macro-block ordering are utilized. At the decoder, a joint approach of a directional interpolation error concealment algorithm and a directional textural motion coherence algorithm is proposed to conceal the corrupted color frames. For the concealment of the lost depth frames, an encoder independent decoder dependent depth-assisted error concealment algorithm is suggested. Moreover, the weighted overlapping block motion and disparity compensation algorithm is exploited to choose the candidate concealment Motion Vectors (MVs) and Disparity Vectors (DVs). Furthermore, an improved recursive Bayesian filtering algorithm is utilized as a refinement stage to smooth the remaining errors in the previously selected candidate MVs and DVs for achieving better 3DV quality. Simulation results on several 3DV sequences show that the proposed algorithms achieve adequate objective and subjective 3DV quality performance at severe packet loss rates compared to the state-of-the-art algorithms.

Distributed Optimal Relay Selection in Wireless Sensor Networks

&lt;p&gt;In wireless distributed sensor networks, one open problem is how to guarantee the reliable relay selection based on the quality of services diversity. To address this problem, we focus on the reliable adaptive relay selection approach and adaptive QoS supported algorithm, based on which we present a Markov chain model, in consideration of different packet states and error control algorithm assignment. The mathematical analyses and MATLAB simulation results show that the proposed relay selection approach could perform better in terms of saturation throughput, reliability, and energy efficiency, compared with the traditional approaches. More importantly, the quality of real-time multimedia streaming is improved significantly, in terms of decodable frame ratio and delay.&lt;/p&gt;

An adaptive cross-layer error control protocol for wireless multimedia sensor networks

In this paper, we introduce a new adaptive cross-layer error control protocol (NAC) for reliable and efficient real-time multimedia streaming in Wireless Sensor Networks (WSN). The proposed protocol uses an adaptive Forward Error Correction (FEC) algorithm in the application layer, a dynamic hybrid FEC/ARQ method, and an Unequal Error Protection (UEP) mechanism in the wireless link layer. Moreover, it dynamically tunes the amount of erasure codes per group of packets based on mean absolute deviation of recently measured average round-trip times, and cross-layer information such as arrival of acknowledgment packets, network traffic load, and wireless channel state. In fact, by dynamically determining suitable redundancy for the source packet based on the packet loss rate in the hybrid FEC/ARQ error control algorithm in the wireless link layer, we achieve significant improvements over the existing error control schemes, while decreasing the number of retransmissions in ARQ mechanism. Our comprehensive simulations show that the proposed NAC protocol achieves better performance compared to the popular error control schemes in terms of energy efficiency, frame loss rate, frame Peak Signal-to-Noise Ratio (PSNR), and delay-constrained PSNR for real-time multimedia streaming over sensor networks.

Integer codes correcting double asymmetric errors

In this study, the authors present a class of integer codes capable to correct single and double asymmetric errors within a codeword. The encoding/decoding procedures for these codes are extremely simple, whereas the error control algorithm resembles the routing process. Thanks to these features, the presented codes have a potential to be practically applied, especially in optical networks covering small areas.